Methods and systems for automated sonic imaging

ABSTRACT

A method is provided for identifying and characterizing structures of interest in a formation traversed by a wellbore, which involves obtaining waveform data associated with received acoustic signals as a function of measured depth in the wellbore. A set of arrival events and corresponding time picks is identified by automatic and/or manual methods that analyze the waveform data. A ray tracing inversion is carried out for each arrival event (and corresponding time pick) over a number of possible raypath types to determine i) two-dimensional reflector positions corresponding to the arrival event for the number of possible raypath types and ii) predicted inclination angles of the reflected wavefield for the number of possible raypath types. The waveform data associated with each time pick (and corresponding arrival event) is processed to determine a three-dimensional slowness-time coherence representations of the waveform data for the number of possible raypath types, which is evaluated to determine azimuth position and orientation of a corresponding reflector, and determine the ray path type of the reflected wavefield. The method outputs a three-dimensional position and/or orientation for at least one reflector, wherein the three-dimensional position of the reflector is based on the two-dimensional position of the reflector determined from the ray tracing inversion and the azimuth position of the reflector determined from the three-dimensional slowness-time coherence representation. The information derived from the method can be conveyed in various displays and plots and structured formats for reservoir understanding and also output for use in reservoir analysis and other applications.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present disclosure claims priority from U.S. Provisional Appl. No.62/576,254, filed on Oct. 24, 2017, commonly assigned to assignee of thepresent disclosure and herein incorporated by reference in its entirety.

BACKGROUND 1. Field

The present disclosure relates to methods and systems that use downholesonic logging measurements to derive information pertaining tosubsurface formation structures.

2. State of the Art

Using downhole measurements to derive information pertaining tosubsurface formation structures continues to be increasing importancefor oil and gas exploration and development. Early in the development ofan oil and gas field, surface seismic and electromagnetic measurementsare particularly helpful for discerning the coarse and large-scalestructures of the subsurface. Furthermore, vertical seismic profile(VSP) measurements and, more recently, electromagnetics logging whiledrilling (LWD) measurements can be used to create images of somewhatfiner scale structures relatively near the wellbore. All of thisinformation can aid oil and gas companies to make improved explorationand development decisions.

The goal of sonic imaging is to use downhole sonic logging measurementsto create high resolution images and mappings of near wellbore formationstructures. This information compliments the comparatively lowerresolution but larger scale formation information and images that can bedetermined by surface seismic measurements and by VSP measurements. Inaddition, the downhole sonic imaging measurements can help complimentsimilar scale measurements provided by the aforementioned LWDelectromagnetics measurements.

Current state of the art sonic imaging methods can be logicallypartitioned into three phases as shown in FIG. 1. In the first phase, anacoustic source (or transmitter) of a downhole sonic tool is operated toemit wavefronts that probe the near wellbore structures (e.g.,fractures, nearby bed boundaries and faults, etc.) by propagating,reflecting, and refracting across these structures, which are generallyreferred to as reflectors in the present disclosure. The downhole sonictool also includes an array of acoustic receivers (or sensors) thatreceive the resulting wavefronts to obtain the sonic waveform data. Inthe second phase, the waveforms are processed to separate out and filterborehole modes and other interfering signals from the waveform data. Inthe third phase, the resultant filtered waveform data is processedcreate an image that conveys the presence of the near wellborestructures. The focus of the processing of the third phase resides onreflections from acoustic impedance boundaries and mode-converted wavesgenerated as compressional or shear waves cross acoustic impedanceboundaries. When imaging an event that crosses the borehole, thegeometry of the tool as well as the location and orientation of theevent relative to the tool axis govern the reflected waveforms presentin the data acquired. If both the transmitter and receivers are locatedabove the event, the generated reflections received by the tooloriginate from the updip side of the event and the reflections from thedowndip side of the event do not return to the tool. When thetransmitter and receiver are below the event, the reflections measuredby the tool originate from the downdip side of the event, and the updipreflections propagate away from the borehole without being detected bythe tool. When the generated reflections received by the tool originatefrom a location between the transmitter and receiver,compressional-to-shear wave (PS) conversions are seen on the updip sideof the event, and shear-to-compressional wave (SP) conversions are seenon the downdip side of the event. Using the knowledge of how thereflected waves behave, in comparison to refracted waves, the events canbe classified. Then, information from the reflected and mode-convertedwaves can be used to create an image of the acoustic impedanceboundaries that cross the borehole or that are situated within the depthof investigation for that particular case. The presence of the nearwellbore structures (reflectors) can be identified from the display ofthe image.

The outcome of the methodology depicted in FIG. 1 is a two-dimensional(2D) migration image of the near wellbore structures presents numerouschallenges for its use with subsequent modeling and interpretationworkflows. First, 2D migration images often fail to capture the 3Dorientations of the near wellbore structures. The near wellborestructures could include fractures which intersect at right angles orcould include fault planes which are not aligned with the formationlayering. A migration image presents all the reflectors as lying in thesame 2D plane. Second, the 2D migration image cannot providequantitative information for the reflectors. Third, a 2D migration imagedoes not provide for determining the coordinates and orientation of thereflectors in three-dimensions (3D) in a manner which can be captured ina digital subsurface model. The 2D migration is just a picture, oftenwith significant noise and artifacts, not a collection of orientedreflectors in 3D.

The simplicity of the methodology depicted in FIG. 1 belies thecomplexity of the interpretation task required to execute this sonicimaging method. Among the tasks required of the interpreter are thefollowing, all of which must be done based on the user's visualinspection of the waveforms and the imaging results.

First, the possible arrival events in the filtered waveform data arelocated. This can be a very labor-intensive portion of the method, asthe user must typically visually inspect waveforms recorded by 4 to 8sensors mounted around the circumference of the downhole sonic toolalong hundreds, if not thousands, of feet in measured depth.

Second, the ray path type of any arrival events observed in the filteredwaveform data is determined. Ray path type refers to the propagationmode (compressional or shear) of the wavefront before and after thewavefront encounters the reflector as shown in FIGS. 2A, 2B and 2C.Reflections from nearby reflectors can follow a PP or SS ray path asseen in FIGS. 2A and 2B, while mode conversion (PS or SP) is oftenobserved when waves refract across planar fractures, as depicted in FIG.2C.

Third, the measured depth locations and azimuths of the correspondingreflectors are determined. If the user plans to create a two-dimensionalmigration image along the well trajectory, choosing the correct azimuthensures that the reflectors are in the imaging plane.

Fourth, the migration apertures required to successfully image thereflectors are estimated. Reflectors which are perpendicular to the welltrack, typically require a long aperture to be successfully imaged.

Fifth, the migration is configured and run based on the parametersdetermined in the first through fourth steps. Since the imaging resultscan vary significantly as these parameters are changed, the usertypically must engage in an iterative procedure until a “convincing”imaging result is obtained. As the migration operations are usuallycomputationally intensive, this step can take a long time.

Sixth, the imaging results are displayed. The accuracy of the imagingresults (or quality control) is often analyzed and explained using thearrival events present in the waveform measurements based on the resultsof the first through fifth steps. Today, there is no standard practicefor providing this quality control.

Besides being time consuming, this workflow is often perceived to bevery subjective. In addition, 2D images produced using this procedure donot provide the locations or orientations of the near wellborereflectors and often do not capture the actual 3D geometry of theformation structures located along the well trajectory.

SUMMARY

The present disclosure provides an automatic methodology that canprocess sonic waveform data to identify the three-dimensional locationand orientation of near wellbore formation structures (reflectors) andoptionally to construct an image of the acoustic impedance boundariesthat shows these near wellbore formation structures (reflectors). Thereflectors can be earth formation layer boundaries, fractures, faults,as well as nearby wellbores. The methodology includes a first automaticprocedure (Tau-P and Event Localization Procedure) that processes thefiltered waveform data to detect candidate arrival events that may ormay not correspond to reflectors. The first automatic procedure can alsobe configured to identify the candidate arrival events that correspondto reflectors (actual arrival events) and filter out candidate arrivalevents that correspond to noise. The methodology also includes a secondautomatic procedure (ray tracing inversion and three-dimensionalslowness time coherence (3D STC) procedure) that can determines the raypath type for one or more of the detected arrival events, determine the3D position and orientation of a corresponding reflector relative to thetool sonde, determine true 3D position and orientation of thecorresponding reflector, and provide an estimate of the relativeprominence of the detected arrival event.

In embodiments, the 3D position of the reflector relative to the toolsonde can be parameterized by a center point of the reflector defined bydimensions of measured depth, radial offset from the axis of the toolsonde, and azimuth angle around the circumference of the tool sonde andborehole, and the orientation of the reflector relative to the toolsonde can be parameterized by a relative dip angle and an inclinationangle of the reflector. Furthermore, the true 3D position of thereflector can be parameterized by a center point of the reflectordefined in a geographical 3D coordinate system (e.g.,latitude/longitude/height with respect to the reference ellipsoidsurface of the earth), a geocentric coordinate system (e.g., theEarth-Centered Earth-Fixed coordinate system, a vertical earthcoordinate system (e.g., elevation or depth with regard to the geoid),or other suitable 3D earth coordinate system. The true orientation ofthe reflector can be parameterized by coordinates of a normal vectorthat extends from the reflector at its center point. The coordinates ofthe normal vector for the reflector can be translated to a true dipangle and true azimuth angle for the reflector.

The results of these two automatic procedures can also be used togenerate and display (1) logs of the detected arrival events showingtheir measured depth location and any of several attributes (ray pathtype, reflector azimuth around the circumference of the borehole,relative dip, reflector true dip angle and true azimuth angle), (2)display maps that convey two-dimensional and three-dimensional spatialpositions of the reflectors along the well track (which can be used forsubsequent subsurface modeling and simulation workflows such as discretefracture network modeling or modeling which ties these sonic imagingreflectors to the reflectors provided by VSP or seismic imaging), (3) aspreadsheet, or some other structured data object(s), that containsinformation describing the three-dimensional locations and orientationsas well as ray path type of the reflectors, and (4) other views (such asa tadpole plot, stereonet plot or Rose diagram) that displaysinformation that characterizes the true dip and azimuth reflectors as afunction of depth along the well track. One or more these other viewscan be displayed in conjunction with other logs, such as an FMI image ofa corresponding interval of measured depth or possibly a tadpolediagram, stereonet plot or Rose diagram of true dip and azimuth that isderived from the FMI image.

The present disclosure also provides an optional procedure thatautomatically generates parameters for migration that can be used toconfigure the migration, which is executed to construct an image ofacoustic impedance boundaries that shows these reflectors. In addition,an optional quality control (QC) procedure can be used to analyze andexplain the migration operations, particularly the reflectors shown inthe display maps.

The automated methodology of the present disclosure can address a numberof shortcomings in the current state of the art processing including (A)the limited utility of 2D migration images for subsequent modeling andinterpretation workflows; (B) its labor intensive quality; (C) thelimited ability to characterize the reflected arrival events observed inthe filtered waveforms, which leads to; (D) the very limited guidance asto how to configure the migration; (E) the limited information as towhich waveform features lead to which features in the migration result(and vice versa); and finally, (F) the perception that today's sonicimaging methodology is highly subjective.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram illustrating current state of the art sonicimaging methods.

FIGS. 2A-2C are schematic illustrations of different ray path types forarrival events that can be observed as part of the filtered waveformdata in the method of FIG. 1.

FIGS. 3A-3D illustrate exemplary arrival event logs that can be producedby the automated workflow of operations of the present disclosure. Thelogs of FIGS. 3A-3D can optionally be displayed in a common displaywindow.

FIGS. 4A-4F illustrate exemplary display logs and maps that conveytwo-dimensional and three-dimensional locations of reflectors along thewell track, which can be produced and displayed by the automatedworkflow of operations of the present disclosure. Some or all of thelogs and display maps of FIGS. 4A-4F can optionally be displayed in acommon display window.

FIGS. 5A-5C, 6A1-6A6, 6B and 6C are plots illustrating a Tau-P transformand an event localization procedure, which can be part of the automatedworkflow of operations of the present disclosure. FIG. 6B is a schematicdiagram of a time pick (dashed black line) where the measured depth of adetected arrival event is shown as the midpoint of the measured depthinterval for the time pick of the detected arrival event.

FIGS. 7A-7D are exemplary graphical user interfaces that can be producedby the results of the automated event localization procedure, which canbe part of the automated workflow of operations of the presentdisclosure.

FIGS. 8A and 8B are exemplary visual representations of the results ofray tracing inversion and three-dimensional slowness-time-coherence (3DSTC) processing for PP ray types, which can be produced by the automatedworkflow of operations of the present disclosure.

FIG. 8C-8E are schematic representations of exemplary parameters used tocharacterize the wellbore environment and reflector position for the raytracing inversion processing as part of the automated workflow ofoperations of the present disclosure.

FIGS. 8F and 8G are schematic representations of exemplary parametersused to characterize a reflector position and orientation in 3D space aspart of the automated workflow of operations of the present disclosure.

FIG. 8H, which includes FIGS. 8H1-8H2, is a table representation of anexemplary time pick and corresponding ray tracing inversion results forPP and SS raypath types produced as part of the automated workflow ofoperations of the present disclosure.

FIGS. 9A and 9B are exemplary visual representations of the results ofray tracing inversion and 3D STC processing for SS ray types, which canbe produced by the automated workflow of operations of the presentdisclosure.

FIGS. 10A and 10B are graphical user interfaces that summarize theresults of ray tracing inversion and 3D STC processing, which can beproduced by the automated workflow of operations of the presentdisclosure.

FIGS. 11A and 11B are graphical user interfaces that summarize theresults of ray tracing inversion and 3D STC processing, which can beproduced by the automated workflow of operations of the presentdisclosure.

FIG. 11C is a tabular representation of the procedure to score or rankthe raypath types of a particular time pick and corresponding reflectorswhich can be produced by the automated workflow of operations of thepresent disclosure.

FIG. 11D, which includes FIGS. 11D1 and 11D2, is a flowchart of the raytracing inversion and 3D STC computation operations which are part ofthe automated workflow of operations of the present disclosure.

FIGS. 12A-12D are exemplary graphical user interfaces that summarize theresults of an event localization procedure and ray tracing inversion and3D STC processing, which can be produced by the automated workflow ofoperations of the present disclosure. The graphical user interfaces ofFIGS. 12A-12D can optionally be displayed in a common display window.

FIGS. 13 and 14 are visual representations of the results of the raytracing inversion and 3D STC processing for certain detected arrivalevents selected from the filtered waveform data.

FIG. 15 is an exemplary graphical user interface that allows a user toset and adjust certain parameters used in the automated workflow ofoperations of the present disclosure.

FIG. 16, which includes FIGS. 16A1-16A3, is a spreadsheet that storesinformation pertaining to reflectors identified from the filteredwaveform data in the automated workflow of operations of the presentdisclosure; the spreadsheet can be presented to a user for betterunderstanding of the reflectors; it can also be used to supplement areservoir model with information regarding the reflectors for reservoiranalysis and/or simulation based upon the improved reservoir model.

FIG. 16B is a view of an exemplary tadpole plot, which can be generatedfrom the results of the automated workflow of operations of the presentdisclosure.

FIG. 16C is a view of an exemplary stereonet plot, which can begenerated from the results of the automated workflow of operations ofthe present disclosure.

FIG. 16D is a view of an exemplary Rose diagram, which can be generatedfrom the results of the automated workflow of operations of the presentdisclosure.

FIGS. 16E, 16F and 16G are schematic illustrations that describe thegeneration of an FMI image based on resistivity measurements of aformation.

FIG. 17 is a high-level flow chart of the automated workflow ofoperations of the present disclosure.

FIGS. 18A and 18B are schematic diagrams that illustrate the effect ofpolarity of the transmitter of the downhole sonic tool on the polarityof the borehole modes acquired by the receiver array of the downholesonic tool.

FIGS. 19A-19D are other exemplary logs of the results of ray tracinginversion and 3D STC processing, which can be produced by the automatedworkflow of operations of the present disclosure. The logs of FIGS.19A-19D can optionally be displayed in a common display window.

FIGS. 20A-20F illustrate other exemplary logs that can be produced bythe automated workflow of operations of the present disclosure. Some orall of the logs of FIGS. 20A-20F can optionally be displayed in a commondisplay window.

FIG. 21 illustrates an example wellsite which can embody parts of themethodology of the present disclosure.

FIG. 22 illustrates an example computing device that can be used toembody parts of the methodology of the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The particulars shown herein are by way of example and for purposes ofillustrative discussion of the examples of the subject disclosure onlyand are presented in the cause of providing what is believed to be themost useful and readily understood description of the principles andconceptual aspects of the subject disclosure. In this regard, no attemptis made to show details in more detail than is necessary, thedescription taken with the drawings making apparent to those skilled inthe art how the several forms of the subject disclosure may be embodiedin practice. Furthermore, like reference numbers and designations in thevarious drawings indicate like elements.

The methodology of the present disclosure presents an alternative to andcan improve upon the operations of the third phase of the sonic imagingmethodology shown in FIG. 1. In particular, an automated workflow isprovided that includes one or more of the following operations:

-   -   (A) A first automatic procedure (Tau-P and Event Localization        Procedure) that processes the filtered waveform measurements to        identify candidate arrival events and to rank the candidate        arrival events according to their relative prominence. The        candidate arrival events may or may not correspond to near        wellbore formation structures of interest (reflectors), which        can be earth formation layer boundaries, fractures, faults, as        well as nearby wellbores. For example, some of the candidate        arrival events may correspond to noise. In embodiments, the        first automatic procedure can determine a time pick for a        candidate arrival event, which may consist of onset or arrival        times of the candidate arrival event for an interval of measured        depths as depicted in FIGS. 6B, 6C, and in panel 701 of FIG. 7A.        These procedures can produce and display a log of candidate        arrival events, for example as shown in the panel of FIG. 3A and        in the panel 703 of FIG. 7D where the candidate arrival events        are shown by markings as function of measured depth on the        vertical axis and receiver sensor azimuthal index around the        tool circumference of the tool sonde on the horizontal axis. The        intensity of each marking depicts the relative prominence of the        corresponding candidate arrival event. The first automatic        procedure can also be configured to identify the candidate        arrival events produced by reflectors (referred to as “actual        arrival events”) and thus filter out the candidate arrival        events corresponding to noise.    -   (B) A second automatic procedure (ray tracing inversion and        three-dimensional slowness time coherence workflow) that        processes the data representing the time pick and corresponding        portion of the filtered wavefield for one or more detected        arrival events (which are preferably one or more detected actual        arrival events that are produced by reflectors and thus do not        correspond to noise) using a ray tracing inversion and        three-dimensional slowness time coherence workflow that        characterizes the raypath types of the detected arrival event        and that determines three-dimensional position and orientation        of the reflector corresponding to the detected arrival event and        corresponding time pick. There can be a number of possible        outcomes of this procedure, such as:        -   a. A display log that conveys the detected arrival events as            a function of measured depth and receiver azimuth index and            ray path type as shown in the panel of FIG. 3B, in the panel            1203 of FIG. 12D, and in panel 1903 of FIG. 19D. The            measured depth of a detected arrival event is the mean            measured depth of the corresponding time pick as conveyed in            FIG. 6B. Each detected arrival event can be denoted by a            marking where the hue of the marking can indicate ray path            type (such as a “Cyan” color representing the ‘PP reflected’            ray path type, a “Brown” color representing the ‘SP            refracted’ ray path type, a “Magenta” color representing the            ‘PS refracted’ ray path type, and an “Orange” color            representing the ‘SS reflected’ ray path type), while the            intensity of the marking can depict the relative prominence            of the corresponding arrival event. The ray path type for            each detected arrival event is determined by the results of            the ray tracing inversion and three-dimensional slowness            time coherence workflow.        -   b. A display log the coveys the position of the reflectors            that produce the detected arrival events as a function of            measured depth and azimuth angle (in degrees) as shown in            the panel of FIG. 3C, in panel 405 of FIG. 4C, in FIG. 4D,            panel 2005 of FIG. 20 and in FIG. 20D. Each reflector can be            denoted by a marking where the hue of the marking can            indicate ray path type (such as a “Cyan” color representing            the ‘PP reflected’ ray path type, a “Brown” color            representing the ‘SP refracted’ ray path type, a “Magenta”            color representing the ‘PS refracted’ ray path type, and an            “Orange” color representing the ‘SS reflected’ ray path            type). The ray path type for each reflector is determined by            the results of the ray tracing inversion and 3D slowness            time coherence workflow.        -   c. One or more display maps with markings that convey the            three-dimensional or two-dimensional locations of the            reflectors that produce the detected arrival events relative            to the well track as depicted by the panels 401 and 403 of            FIGS. 4A and 4B, by FIG. 4F, by the panels 2001 and 2003 of            FIGS. 20A and 20B, and by FIG. 20F.        -   d. A spreadsheet (FIGS. 16A1-16A3), or some other structured            data object(s), that contains information describing the            three-dimensional locations and orientations as well as ray            path type of the reflectors.        -   e. Other views that display information that characterizes            the 3D orientation of the reflectors. Such other views can            include a tadpole plot (FIGS. 4E 16B, and 20E), a stereonet            plot (FIG. 16C), and/or a Rose diagram (FIG. 16D). One or            more these other views can be displayed in conjunction with            other logs, such as an FMI image of a corresponding interval            of measured depth or possibly a tadpole diagram, stereonet            plot or Rose diagram of dip that is derived from the FMI            image.    -   (C) A third automatic procedure that groups detected arrival        events together and generates the appropriate parameter choices        required for the migration. This grouping can be done according        to ray path type, reflector azimuth, and estimated migration        aperture. The results of this grouping operation are shown in        the panel of FIG. 3D. If there are detected arrival events with        different ray path types present in the waveform measurements,        multiple migrations can be configured automatically for the user        to review. The user can review and assess the quality of the        automatically generated migration parameters using a dialogue        box such as is depicted in FIG. 15.    -   (D) A fourth automatic procedure that automatically generates        markings for quality control that are associated with particular        features in the migration image and with particular features in        the filtered waveform data. Examples of such markings are        depicted in panel 403 of FIG. 4B. Again, the hue and intensity        of the QC markings, which are overlaid on the resulting image,        can refer to the ray path type and relative prominence of the        corresponding actual arrival event.

Note that the display logs and panels can label the detected arrivalevents or reflectors and associated markings with numerical indices orother labels to help users identify common arrival events or reflectorsdepicted across multiple logs and panels.

This automated workflow of the operations can improve the quality andefficiency of the sonic imaging interpretation. In particular, theautomation can strongly reduce the interpretation's subjectivity thatfrequently reduces confidence in the imaging result. In addition, thedisplay logs shown in FIGS. 3A-3D and elsewhere in this disclosure canlead the user to a whole new perspective on the interpretation and canreveal features that were previously overlooked.

Moreover, the 2D migration image typically provides limited quantitativeinformation of the reflectors. The automated workflow of the operationscan provide valuable quantitative information of the reflectors, such astrue 3D positions and true 3D orientations of the reflectors. Inembodiments, the true 3D position of a reflector can be parameterized bya center point of the reflector defined in a geographical 3D coordinatesystem (e.g., latitude/longitude/height with respect to the referenceellipsoid surface of the earth), a geocentric coordinate system (e.g.,the Earth-Centered Earth-Fixed coordinate system, a vertical earthcoordinate system (e.g., elevation or depth with regard to the geoid),or other suitable 3D earth coordinate system. The true 3D orientation ofthe reflector can be parameterized by coordinates of a normal vectorthat extends from the reflector at its center point. The coordinates ofthe normal vector for the reflector can be translated to a true dipangle and true azimuth angle for the reflector. Logs of true dip angleand true azimuth of the reflectors in the form of tadpole logs,steronets and Rose diagrams can be generated and displayed. Such logsare very valuable because they allow for a subsequent interpretation andcomparison with the respective true dip angle and azimuth angle of thereflectors derived from borehole images such as FMI.

Furthermore, the automated workflow of the operations can determine thecoordinates and orientation of the reflectors in 3D in a manner whichcan be captured in a digital subsurface model. The 2D migration is justa picture, often with significant noise and artifacts, not a collectionof oriented reflectors in 3D. Collecting the reflectors in a 3D modelcan be very helpful for discrete fracture network modeling of fluid flowor for tying the near wellbore reflectors to the larger scale reflectorsdetermined from VSP or seismic measurements. For these and otherreasons, the automated workflow of the operations can allow the operatorto avoid using a migration for sonic imaging.

FIGS. 3A-3D illustrate exemplary display logs that can be generated anddisplayed by the automated workflow of operations. In the panels ofFIGS. 3A and 3B, the markings indicate measured depth and receiverazimuth index for detected candidate arrival events (FIG. 3A) and fordetected arrival events that are produced by reflectors (FIG. 3B). Inthe panels of FIGS. 3C and 3D, the markings indicate measured depth andazimuth angle for the reflectors that produce the detected arrivalevents. In the panels of FIGS. 3A-3B, the relative prominence of thearrival event is indicated by intensity or saturation of the marking,which is given by the relative score of each arrival event. In thepanels of FIGS. 3B-3D, the color hue of the markings can indicate theray path type of the corresponding arrival event or reflector. The raypath type for each arrival event and corresponding reflector isdetermined by the results of the ray tracing inversion andthree-dimensional slowness time coherence workflow as described herein.

FIGS. 4A-4F illustrate exemplary display logs and maps that conveytwo-dimensional and three-dimensional locations of reflectors thatproduce the detected arrival events along the well track, which can begenerated and displayed by the automated workflow of operations of thepresent disclosure. In embodiments, the display maps of FIGS. 4A-4F canbe generated and displayed side-by-side one another as part of unitarydisplay window.

The display map of panel 401 of FIG. 4A and the display map of FIG. 4Fconvey the three-dimensional locations (in measured depth, radialoffset, and azimuth) of the reflectors relative to the tool axis (welltrack). The tool axis (well track) is depicted as a solid black verticalline as shown. The display map of panel 401 of FIG. 4A displays themeasured depth versus radial offset and azimuth relative to the toolaxis for the reflectors.

The display map of panel 405 of FIG. 4C displays the measured depthversus azimuth angle measured relative to the tool axis (in degrees) ofthe reflectors that fall within a user-defined azimuthal range orwindow. More particularly, the user-specified azimuthal window includestwo parts 411, 413 which are offset from one another by 180° of azimuth.The one part 411 is defined by a pair of solid line segments, and theother part 413 is defined by a pair of dashed vertical line segments.The width or size of each part 411, 413 (in degrees of azimuth) is auser defined parameter. Furthermore, the user can adjust the centerpositions for the two parts 411, 413 (which are offset from one anotherby 180° of azimuth) using the slider bar 406 in FIG. 4C. Each reflectorthat produces a detected arrival event and that is positioned withinthis two-part user-specified azimuthal range can be displayed as part ofthe 2D display map of panel 403 of FIG. 4B.

The map of panel 403 of FIG. 4B is constructing by overlaying the map ofthe reflectors that produce the detected arrival event(s) and that arepositioned within the two-part user-specified azimuthal range (which isspecified by the user in conjunction with panel 405 of FIG. 4C) on the2D migration result relative to the tool axis (well track). The toolaxis is depicted as a solid black vertical line as shown. The map ofpanel 403 of FIG. 4B displays the measured depth versus radial offsetrelative to the tool axis for the reflectors that fall within theuser-defined azimuth range shown and described above with respect to thepanel 405 of FIG. 4C.

FIG. 4E is a tadpole log which describes the true 3D dip and azimuth ofthe reflectors that produce the detected arrival event(s) as a functionof measured depth along the well track. The x position of the tadpoleindicates the true dip and the reflector with 0° representing areflector which is horizontal and 90° representing a vertical reflector.The orientation of the tadpole tail represents the true azimuth of thereflector with 0° representing True North. Stereonet and Rose diagramssuch as those displayed in FIGS. 16C and 16D can be used to summarizethe information presented in the tadpole log of FIG. 4E.

Note that the slider bar 407 of FIG. 4A determines the start of themeasured depth interval of the actual arrival events used for the panelsof FIGS. 4A-4F, while the slider bar 408 determines the lower bound onthe scores of the detected arrival events that are considered actualarrival events produced by reflectors (and not filtered out ascorresponding to noise) and thus used for the panels of FIGS. 4A-4F. Thepanels of FIGS. 4A, 4C, 4D, 4E, and 4F can provide the user with a quicksummary of the three-dimensional spatial positions of the reflectorsthat produce the detected arrival event(s) without having to migrate thewaveform measurements.

Tau-P and Event Localization Procedure

This procedure is applied to views (or selections) of the filteredwaveform data that is measured by a single acoustic receiver over timesuch as is depicted in FIG. 5A. This procedure processes the selectedwaveform data to detect and collect a number of candidate arrival events(which may or may not be produced by reflectors) and to identify timepicks for the detected candidate arrival events. For example, some ofthe candidate arrival events may not be produced by reflectors but bynoise. One or more of the time picks and corresponding waveformmeasurements for the candidate arrival events are analyzed with the raytracing and 3D STC procedure described later in order to determine theray path type produced by the reflectors (and associated with thecorresponding detected arrival events) and to determine thethree-dimensional spatial locations and orientations of the reflectors.

The following procedure is used to construct a single sensor view of thewaveform measurements. At each measured depth position of the acousticlogging tool sonde, a recording is made by a receiver sensor in theacoustic logging tool's receiver array. The signal recorded by thisreceiver sensor over time is placed as a column of a matrix whosecolumns are indexed by measured depth along the well track and whoserows are indexed by recording time. This matrix can be visualized as animage such the image in FIG. 5A or the image 701 in FIG. 7A or the image1201 in FIG. 12A or the image 1901 in FIG. 19A. If the tool is notrotating significantly during a particular measured depth interval,these single sensor waveform views show the response of reflectors onthe side of the tool corresponding to the azimuth of this singlereceiver sensor. If the tool is rotating significantly, an alternativesingle sensor view can be constructed as follows. The 360 degrees aroundthe circumference of the acoustic logging tool sonde can be divided into4 or 8 or some other number of quadrants. For each quadrant, a view ofthe waveforms can be constructed where at each measured depth location,the corresponding column is made using the trace recorded by the singlesensor whose azimuth falls within that azimuth quadrant. Thisconstruction ensures that the “single sensor view” of the waveformsshows the response of the reflectors on the side of the toolcorresponding to this particular azimuth direction.

The Tau-P and Event Localization procedure can be configured by the useraccording to characteristics of the events observed in the filteredwaveform data. For example, the user can choose the time domain in whichto conduct this portion of the workflow. For example, the user canchoose to look for candidate arrival events that arrive between theonset of the direct compressional and the direct shear arrivals(indicated by the lines labeled in FIG. 5A). Alternatively, the timewindow commencing after the onset of the Stoneley arrival (as indicatedby the line labeled in FIG. 5A) can be selected. While examining thecandidate arrival events shown in FIG. 5A, the user can also specify themaximum or steepest event moveout. Typical values are 400 us/ft., where0 us/ft. refers to events that would appear horizontal in FIG. 5A.Lastly, the Tau-P and Event Localization procedure can be applied tofairly short measured depth windows in the single sensor views of thewaveform data. The default depth value for the depth windows is 100foot, but a shorter depth value for the depth windows can be used if theformation slowness is changing rapidly as a function of measured depth.In embodiments, the depth value for the depth windows can be specifiedby user input.

Following this configuration, the filtered waveform data within eachmeasured depth window of a single sensor view is processed using a tau-Ptransform, which is well known in geophysics and described in Diebold,John B.; Stoffa, Paul L. (1981). “The traveltime equation, tau-pmapping, and inversion of common midpoint data,” Geophysics 46 (3):238-254. The tau-P transform is frequently used for processing seismicand VSP measurements. Note that the application of the tau-P transformconverts that filtered waveform data into the time-slowness (tau-P)domain. A subset of peaks in the tau-P domain are selected according totheir relative height. As part of the configuration procedure, the usercan prescribe a minimum separation distance between the peaks to avoidan excessive number of peaks for any particular waveform arrival event.The results of this tau-P transform include a set of lines thatcorrespond to selected peaks in the tau-P domain. In FIG. 5A, the longgreen lines correspond to various tau-P peaks, while the shorter timewindows found along those lines (and marked with particular indexvalues) designate the candidate arrival events determined from the eventlocalization procedure which is described next.

FIG. 5A illustrates the Tau-P and Event Localization Procedure. Thegreen lines overlaid on the displayed waveform data correspond to peaksin tau-P transform of the filtered waveform data. Boxes shown in blackindicate regions along the lines where the localization procedureidentifies a candidate arrival event location. These boxes can belabeled with numbers or other indices that indicate the relative rank orprominence of the candidate arrival event.

FIGS. 5B and 5C illustrate the application of the tau-P transform. FIG.5B shows waveform data in the recorded wavefield domain, while FIG. 5Cshows data in the τ-p Domain. The tau-p transform maps the waveformmeasurements parameterized by measured depth (ft) and recording time(ms) to the tau-p domain which is parameterized by event moveout (us/ft)and time (ms). A peak in the tau-p domain corresponds to a line in thetwo-dimensional wavefront domain (time and measured depth). This tau-Pline is specified by a slope and y-intercept time where the slope isgiven by the event moveout of the peak and the y-intercept time is givenby the recording time of the peak. The peaks in the tau-p domain provideinformation as to the line along which the candidate arrival event ispresent, but do not identify a particular measured depth location forthe candidate arrival event along the line, so that an eventlocalization procedure is needed.

To determine the actual measured depth location of the candidate arrivalevents along the corresponding tau-P lines, an event localizationprocedure is performed as follows. A time window corridor is defined foreach line in the set of lines that correspond to selected peaks in thetau-P domain. The time window corridors have a fixed time duration. Forexample, the time window corridors can have a duration 0.2 ms when usingan 8 kHz monopole acoustic transmitter, or of duration 0.8 ms when usinga lower frequency dipole acoustic transmitter. The time window corridorfor a given line is defined to cover time before, at and after they-intercept time of the line as shown in FIG. 5B. The single sensor viewof the filtered waveform data is resampled at each measured depthlocation to select the filtered waveform data within each time windowcorridor and provide wavefield data arranged along two dimensions,recording time and measured depth within the time window corridor.

The wavefield data for an exemplary set of time window corridors aredisplayed in the subpanels of FIGS. 6A1-6A6 where such time windowcorridors correspond to the tau-P line segments shown as the linesegments in FIG. 5A. In each subpanel of FIGS. 6A1-6A6, the recordingtime within each time window corridor is shown along the left-sidevertical axis, while measured depth within the time window corridor isalong the horizontal axis. The event localization procedure identifieslocation of the candidate arrival event within each time window corridorby correlating the wavefield data to a function that appears as theblack curves superimposed on the subpanel images of FIGS. 6A1-6A6 asfollows. If we designate the wavefield data values within this timewindow as w(i,j) with i being the time index and j being the measureddepth index, we can define a coherent energy E_(c)(j), incoherent energyE_(i)(j), and coherence functions coh(j) as follows.

$\begin{matrix}{{E_{c}(j)} = {\sum\limits_{i = 1}^{T}( {\sum\limits_{\Delta = {{- M}/2}}^{M/2}{w( {i,{j + \Delta}} )}} )^{2}}} & (1) \\{{E_{i}(j)} = {\sum\limits_{i = 1}^{T}{\sum\limits_{\Delta = {{- M}/2}}^{M/2}{w^{2}( {i,{j + \Delta}} )}}}} & (2) \\{{{coh}(j)} = \sqrt{\frac{E_{c}(j)}{{MEi}(j)}}} & (3)\end{matrix}$

Here T is the duration of the time window (0.2 ms/20 us=10 samples inthis case, as the waveforms have a 20 us sampling rate), and M is thelength of a short window in measured depth along the horizontal axis ofthe subpanels that is much shorter than the length of the 100 footlength of the tau-P domain. The black curves overlaid in the panels arenormalized versions of the curve defined as

(j)=E_(c)(j)coh(j). If we designate max

as the maximum value that

attains, and j_(max) the measured depth index where that maximum isattained, then the event localization procedure consists of finding thelast index j_(start)<j_(max) for which

(j)<F max

as well as the first index j_(stop)>j_(max) for which

(j)<F max

where 0<F<1 is a user defined parameter whose default value is 0.2. Thetwo indices j_(start) and j_(stop) are shown as white dots in thesubpanels of FIGS. 6A1-6A6 and define the interval of measured depthwithin each time window corridor wherein each candidate arrival event islocated. This measured depth interval within the time window corridor isindicated by the black box around the arrival event in the schematicdiagram of FIG. 6B and the black boxes along the line segments shown inFIG. 5A. The centerline of these boxes is described as the time pick forthe associated candidate arrival event as shown in FIG. 6B, and themidpoint of the measured depth interval along the time pick is declaredto be the measured depth of the candidate arrival event. FIG. 6C is atabular representation of the time pick for event 31 in FIG. 5A. Thepeak value E_(c)(j_(max))coh(j_(max)) can be defined to be the score ofthe candidate arrival event, and this score can be to rank and possiblytheir corresponding index value. The score for the candidate arrivalevent can also possibly be used to filter the localized candidatearrival events. Specifically, the score can be compared to a minimumthreshold score value for the filtering. For example, if the score isgreater than the minimum threshold score value, the correspondingcandidate arrival can be added to a list of detected arrival events thatare produced by reflectors. However, if the score is less than or equalto the minimum threshold score value, the corresponding candidatearrival event can be determined to correspond to noise and it isfiltered or removed from the list of detected arrival events.

The tau-P and event localization procedure can be applied to thefiltered waveform data recorded over time by each acoustic receiver in aring of acoustic receivers around the circumference of the downholesonic tool. This is helpful because a reflection will sometimes appearmore prominently to receivers facing the corresponding reflector than toreceivers facing away from the reflector. A display log that summarizesthe results of this procedure can be generated for display and displayedas shown in FIG. 3A as well as in the panel 703 of FIG. 7D. In thesedisplay logs, each acoustic receiver in a ring of acoustic receiversaround the circumference of the downhole sonic tool is assigned anazimuth index number from 1 to 8 and the candidate arrival events forthe respective acoustic receivers are displayed as function of measureddepth along the Y-axis in separate columns that correspond to acousticreceiver indices along the X-axis. The measured depth of a candidatearrival event is defined as the average measured depth location of thecorresponding time pick as shown as item 601 in FIG. 6B.

FIGS. 7A-7D show exemplary graphical user interfaces produced by theresults of the automated Tau-P and Event Localization procedure. Thepanel 701 of FIG. 7A shows the filtered waveform data recorded by the5th azimuth sensor in the 7th receiver ring along with the candidatearrival events determined by the Tau-P and Event Localization procedurewhose score is larger than 2.63, a value that is set by the user usingthe slider bar 705. The panel 703 of FIG. 7D indicates the measureddepth and receiver azimuth index of the candidate arrival events whosescore is larger than 2.63. Note that panel 703 of FIG. 7D has one boxthat highlights one-fifth of eight columns in panel 703 which shows themean measured depth locations of arrival events found in the filteredwaveform data recorded by that particular sensor, and theintensity/opaqueness of the corresponding grayscale boxes is determinedaccording to their score. Also note that panel 703 of FIG. 7D hasanother box that highlights a measured depth interval from X090 to X290for all eight receiver sensors. This measured depth interval correspondsto the horizontal axis of panel 701 of FIG. 7A. The panels of FIGS. 7Aand 7D can include labels that give the numerical rank of thecorresponding candidate arrival event along with the score. Finally, theslider bar 705 can be adjusted by the user to set the minimum thresholdscore that is used to control or filter the candidate arrival eventsthat are added to the list of detected arrival events produced fromreflectors and shown in the waveform viewer of panel 701 and log panel703.

Note that alternative and additional procedures could be employed toautomatically detect and locate the candidate arrival events in thefiltered waveform data. For example, in some cases it is useful tomultiply the waveform values by a geometrical spreading function such asw_(boost)(t)=w_(orig)(t)t^(1.2), to boost the amplitudes of arrivalevents. Additionally, a Hough transform instead of a Tau-P transformcould alternatively be used to locate arrival events within the singlesensor view of the filtered wavefield.

Automated Ray Tracing Inversion and 3D Slowness Time Coherence Procedure

The outcome of the tau-P and event localization procedure is acollection of time picks for a number of detected arrival events for oneor more acoustic receivers of the tool. The time pick and correspondingwavefield of a given detected arrival event can be processed using raytracing inversion and 3D STC processing to characterize the ray pathtype of the detected arrival event, determine the azimuth of thecorresponding reflector relative to the well track, and determine thethree-dimensional position and orientation of the correspondingreflector. This procedure can also (a) construct logs of summarycharacteristics of the reflectors as a function of measured depthincluding reflector azimuth, relative dip, true dip and azimuth, (2)construct the display maps of the two-dimensional and three-dimensionallocations of the reflectors relative to the well track (including anestimate of its relative dip), (3) produce a spreadsheet (or some otherstructured data object(s)) that contains information describingthree-dimensional location and orientation as well as ray path type ofthe reflectors (for example, see FIG. 16 as described below), (4)provide tadpole plots, stereonet plots, Rose diagrams describing thetrue 3D orientation of the reflectors, and (5) provide an estimate ofthe migration aperture required to construct an image of the acousticimpedance boundaries associated with the reflectors based on the arrivalevents.

The user can configure this procedure by choosing the set of possibleray path types, such as those depicted in FIGS. 2A-2C. The defaultchoice is to enable PP and SS reflections as well as PS and SPrefractions, but if the user chooses only to look for events comingafter the shear, then the user should only look for PP and SSreflections. If the time domain selected by the user in the time pickingworkflow was between the onset of the direct compressional and sheararrivals, then only the PP, PS, and SP ray path types would need to beincluded.

The ray tracing inversion step determines the 2D position of a reflectorrelative to the well track using the time pick associated with adetected arrival event and a choice of ray path type. This ray tracinginversion is repeated using the time pick associated with a detectedarrival event and the set of possible ray path types. FIG. 8A shows thePP ray tracing inversion result for arrival event #4 whose time pick isshown by the line segment in 1201 of FIG. 12A and shown in the first twocolumns of the table in FIGS. 8H1-8H2. FIG. 9A shows the SS ray tracinginversion result for the same arrival event #4's time pick. The circlesrepresent the measured depth positions of the acoustic logging tool'ssource (or transmitter) at the various measured depth locations alongthe time pick, while the black squares represent the measured depthposition of the acoustic logging tool's seventh receiver ring at thevarious measured depth locations along the time pick. These locationsare listed in third and fourth columns of the table in FIGS. 8H1-8H2.The black stars represent the inverted position of the reflector shownrelative to the axis of the tool sonde which corresponds to the x-axesof FIGS. 8A and 9A. The raypaths connecting the source to the reflectorto the receiver (for example, the ones shown as a dashed line segmentsin FIGS. 8A and 9A) describe the raypaths along which the reflected wavepropagated. The positions of the reflection points along the reflectorfor each source and receiver location is determined using the raypathtype and Snell's law.

When using a PP or SS reflection ray path type, the ray tracinginversion varies the two-dimensional position of a linear reflectorspecified by a polar angle α and polar distance d from a point Pcomputed as the average measured depth position of the source andreceiver positions along the time pick as shown in FIG. 8D. For the PPand SS raypath types, the polar angle α of the reflector is related tothe relative dip angle β of the reflector by the relation β=α−90 asshown in FIG. 8D. Thus, the ray tracing inversion varies the relativedip angle β by changing the two-dimensional position of the linearreflector.

When using a SP or PS mode conversion ray path type, the ray tracinginversion varies both two-dimensional position and relative dip of alinear reflector. The two-dimensional position of the reflector isspecified by a point P located between the source and receiver positionsalong the time pick, and the relative dip of the linear reflector isspecified by polar angle α as shown in FIG. 8E. For the PS and SPraypath types, the polar angle α of the reflector is related to therelative dip angle β of the reflector by the relation

$\beta = {\begin{Bmatrix}\alpha \\{\alpha - 180}\end{Bmatrix}\begin{matrix}{{{when}\mspace{14mu} \alpha} < {90{^\circ}}} \\{{{when}\mspace{14mu} \alpha} > {90{^\circ}}}\end{matrix}}$

as shown in FIG. 8E.

For each two-dimensional reflector position (for SS, PP, SP and PSraypath types) and possibly a number of different reflector dip anglesat that 2D reflector position (for SP and PS raypath types), a raytracing between the source, reflector, and receiver positions similar tothe ray tracing shown in FIGS. 8A and 9A is performed which obeysSnell's Law. Then the predicted travel times between the source andreceiver positions is calculated and compared to the travel timesrepresented by the time pick. These predicted travel times are computedfor each ray path as the sum of the travel time between the source andthe predicted point on the reflector and the travel time between thepredicted point on the reflector and the receiver position. For the PPraypath ray tracing inversion, these travel times are computed as theproduct of the distance from the source to the reflector times the Pformation slowness plus the product of the distance from reflector tothe receiver times the P formation slowness. For the SS raypath raytracing inversion, computation of the predicted travel times uses the Sformation slowness. The ray tracing inversion for each ray path type canutilize a gradient search or other inversion algorithm to search for areflector two-dimensional position and relative dip angle where thepredicted travel times best fits the time pick for the detected arrivalevent according to the minimization criterion given in Equation 4 below.

$\begin{matrix}{( {\hat{\alpha},\hat{d}} ) = {\min\limits_{\alpha,d}\sqrt{\frac{\sum_{j}( {{tt}_{j,{pick}} - {tt}_{j,{pred}}} )^{2}}{\sum_{j}( {tt}_{j,{pick}} )^{2}}}}} & (4)\end{matrix}$

-   -   where tt_(j,pick) is the travel time from source to reflector to        receiver based on the time pick for the detected arrival event,        and    -   tt_(j,pred) is the is the predicted travel time from source to        reflector to receiver.

In FIGS. 10A and 11A, the graphs of the detected time pick as a functionof measured depth are compared to the graphs of the total travel times(or predicted time pick) as a function of measured depth as determinedby the ray tracing inversion for the corresponding reflector positionsshown in FIGS. 8A and 9A, respectively and observe that the agreement isvery close. We can quantify this agreement using a relative errormetric.

$\begin{matrix}{\Lambda = {{\Lambda ( {\hat{\alpha},\hat{d}} )} = \sqrt{\frac{\sum_{j}( {{tt}_{j,{pick}} - {tt}_{j,{pred}}} )^{2}}{\sum_{j}( {tt}_{j,{pick}} )^{2}}}}} & (5)\end{matrix}$

The relative errors of the PP and SS ray tracing inversion results shownin FIGS. 10A and 11A are 0.2% and 0.19% respectively.

Note that the linear reflectors determined by the PP and SS raytracinginversions and shown in FIGS. 8A and 9A as well as in columns 5-6 and7-8 of the table in FIG. 8H, respectively, are placed in very differentspatial positions relative to the well track along the horizontal axisfor the PP and SS ray path types. However, the ray tracing inversionresults fit the travel time pick equally well for the PP and SS ray pathtypes as indicated by the data error percentages in the titles of thepanels 801 and 901 of FIGS. 8A and 9A. This means that the ray tracinginversion by itself may not be sufficient to determine the raypath typeof the reflected arrival event. That is, the time pick information maynot be sufficient to determine the raypath type of the reflected arrivalevent. Thus, an analysis of the wavefield along the time pick isrequired. We accomplish this using the 3D slowness time coherenceworkflow which is described next.

In other embodiments, the ray tracing inversion can also invert timepicks according to refraction raypaths as illustrated in FIG. 8E wherethe position of the two-dimensional refractor is determined by a point Palong the measured depth axis and the angle α made by the intersectionof the refractor and the measured depth axis.

In other embodiments, the prediction of travel time can possibly accountfor refraction of the wavefront across a borehole fluid/borehole wallinterface in the ray path of the acoustic wave between the transmitterand the reflector and for refraction of the wavefront across a boreholewall/borehole fluid interface in the ray path of the acoustic wavebetween the reflector and the point in the measured depth interval.Details of certain operations of the ray tracing inversion are set forthin U.S. Pat. No. 9,529,109B2, entitled “Methods and apparatus fordetermining slowness of wavefronts.”

Note the reflector positions solved by the ray tracing inversion arespecified by two-dimensional positions and relative dips of thereflector relative to the well track, and do not include azimuthalinformation. In order to derive azimuthal information for the reflectorsand also resolve the case where separate inversions solve for differentreflector locations for the different ray path types, the second stagein this automated procedure is carried out using 3D STC processing (asoutlined below) to study the wavefield of the arrival event at themeasured depth positions along the time pick.

While the ray tracing inversion may not determine the ray path type orprovide azimuth information, it does provide valuable information as tothe range of inclination angles at which the wavefield should beexpected to arrive at the receiver array. FIG. 8A describes thewavefield propagation of the reflection from source to reflector toreceiver array under the PP reflection raypath hypothesis. Suchwavefield propagation indicates that a P wave should be expected toimpinge on the receiver array with an incidence angle of 88° at thearrival time specified by the time pick. Similarly, FIG. 9A describesthe wavefield propagation for the SS reflection raypath hypothesis. Suchwavefield propagation indicates that a S wave should be expected toimpinge on the receiver array with an incidence angle of 75° at thearrival time specified by the time pick. Thus, to distinguish theraypath type of the detected arrival event, 3D STC computations can beused to assess whether a P wave or S wave is observed at the arrivaltime specified by the time pick at the incidence angles prescribed bythe corresponding ray tracing inversion. We shall observe that the 3DSTC computations also provide azimuth information for this impingingwavefield.

As part of the 3D STC computations, 3D slowness can be parameterized inspherical coordinates, so that the 3D STC coherence function is given bycoh(τ, s_(r), θ, φ). The parameter τ is the onset time of the arrivalevent and is determined from the event time pick. The parameter s_(r) isthe propagation speed of the arrival event at the receiver array and isdetermined from the slowness logs derived from the waveforms as part ofstandard P&S logging at the wellsite. For the PP ray tracing inversionshown in FIG. 8A, the parameter s_(r) is the average P formationslowness at the measured depths determined from the time pick. For theSS ray tracing inversion shown in FIG. 9A, the parameter s_(r) is the Sformation slowness at the measured depth determined from the time pick.In general, if XY is the raypath type for the arrival event, the mean Yformation slowness near the event measured depth is used for the 3D STCcomputation. The parameter θ is an inclination angle made between thetool axis and direction of propagation of the reflected wavefield whenit arrives at the receiver array where θ of 0° represents thepropagation along the tool axis from transmitter (source) to thereceiver array. The center of the range of inclination angles θ isdetermined as the inclination angle predicted from the ray tracinginversion, while the width of the range of inclination angles is a userdefined parameter whose default value is 25°. For the PP raytracinginversion shown in FIG. 8A, the range of inclination angles used is88°−25°=63° to 88°+25°=113°, while for the SS ray tracing inversionshown in FIG. 9A, the range of inclination angles used is 75°−25°=50° to75°+25°=100°. The parameter φ is an azimuthal angle that representsazimuth around the circumference of the tool sonde where φ of 0° is topof hole in the case of a horizontal well or True North in the case of avertical or deviated well. Since the ray tracing inversions do notprovide any azimuth information, the range of azimuth angles used isbetween −180° and 180° or between 0° and 360°.

The 3D STC computations can be performed for a given detected arrivalevent and one or more possible raypath type (e.g., SS, PP, SP, and PSray types) to assess the impinging wavefield at the slowness andinclination angles predicted by the corresponding ray tracing inversionfor the one or more possible raypath types at each measurement stationalong the time pick. FIG. 8B shows the 3D STC computation resultcorresponding to the PP ray tracing inversion shown as FIG. 8A for themeasurement station X1256 ft described by the dashed ray path. FIG. 9Bshows the 3D STC computation result corresponding to the SS ray tracinginversion shown as FIG. 9A for the same measurement station X1256 ftdescribed by the dashed ray path. We observe a well-defined peak withvalue of 0.43 in FIG. 8B that is located at an inclination angle 84°which close to the inclination angle of 88° predicted from the PP raytracing inversion °, a difference of only 4°. In addition, we observethat in FIG. 8B, the azimuth angle φ of the peak is around 30°. Incontrast, the peak appearing in FIG. 9B is not as well defined and hasonly a value of 0.2. Moreover, the inclination angle of the peakappearing in FIG. 9B is 86°, whereas the inclination angle predicted bythe SS ray tracing inversion is only 75°, a difference of almost 11°.Thus, for measurement station X1256 ft, the 3D STC computations indicatethat the impinging wavefield appears to be more consistent with the PPray path interpretation at the measurement station X1256 ft along thetime pick. To make a comprehensive evaluation of the raypath type forthe detected arrival event and an optimal estimate for the azimuth φ ofthe arrival event's impinging wavefield, the 3D STC computations,specifically an assessment of the 3D STC peak location and peak values,need to be performed and summarized for each possible raypath type atall the measurement depth positions along the arrival event's time pick.

FIG. 10B and FIG. 11B show scatterplots of the 3D STC peak locations forthe PP and SS raypath types for arrival event #4, respectively. The 3DSTC peak locations for the PP raypath type in FIG. 10B are welllocalized, whereas the 3D STC peak locations for the SS raypath typeshown in FIG. 11B are scattered, particularly in the azimuth directionalong the horizontal axis. To quantify the measure of scatter in the 3DSTC peak locations, we may use angular dispersion which for a generalset of angles {α_(j)} is given by the following angular mean anddispersion formulas.

$\begin{matrix}{\overset{\_}{\alpha} = {\frac{180}{\pi}a\; \tan \; 2( {{\sum\limits_{j}{\sin \mspace{11mu} \alpha_{j}}},{\sum\limits_{j}{\cos \mspace{11mu} \alpha_{j}}}} )}} & (6) \\{\sigma_{\alpha} = {\frac{1}{N}\sqrt{{\sum\limits_{j}{\sin^{2}( {\alpha_{j} - \overset{\_}{\alpha}} )}} + {\cos^{2}( {\alpha_{j} - \overset{\_}{\alpha}} )}}}} & (7)\end{matrix}$

Values of σ_(α) are between 0.0 and 1.0 with values close to 1.0indicating that the values of {α_(j)} are well localized and with valuesclose to 0.0 indicating more scattered values of {α_(j)}. In FIG. 10B,we observe that the angular dispersions of the 3D STC peaks for the PPraypath type for azimuth is σ_(φ)=0.87 and for inclination angle isσ_(θ)=0.89. In FIG. 11B, we observe that the angular dispersions of the3D STC peaks for the SS raypath type for azimuth is σ_(φ)=0.34 and forinclination angle is σ_(θ)=0.78. These angular dispersion measures showthat the 3D STC peaks for the PP raypath interpretation of arrival event#4 are much more focused that are the 3D STC peaks for the SS raypathinterpretation of arrival event #4. Finally, it is also important tonote that the mean azimuth estimate φ=41.1° for the PP raypathinterpretation provides the reflector azimuth corresponding to arrivalevent #4.

Besides using the angular dispersion to assess the 3D STC peak locationsto assess the arrival event's raypath interpretation, assessing the 3DSTC peak values themselves is also very helpful, because these indicatethe coherence of the event's impinging wavefront at the slowness andrange of inclination angles prescribed by corresponding ray tracinginversion. In FIG. 10A, the sum of the 3D STC peak coherence values is13.5 for the PP raypath interpretation of arrival event #4, whereas thesum of the 3D STC peak coherence values is 6.7 for the SS raypathinterpretation of arrival event #4. Thus, this sum of 3D STC peak valuesalso indicates that the PP raypath interpretation is more valid than isthe SS raypath interpretation of arrival event #4.

We have used the ray tracing inversion and 3D STC computations to assessthe possible SS and PP ray path type interpretations for arrival event#4. We used the relative error of Equation 5 to assess the ray tracinginversion's fit to the data (which is denoted Λ) as well as the angulardispersion estimates σ_(φ) and σ_(θ) obtained from Equations 6 and 7applied to the 3D STC peak locations. Finally, we have also addedtogether the 3D STC peak amplitudes p_(j) to assess the overallcoherence of the arrival event. The information obtained from the raytracing inversion and 3D STC analysis can be combined using thefollowing scoring function:

$\begin{matrix}{{{event}\mspace{14mu} {score}} = {\sigma_{\theta}*\sigma_{\phi}*{\exp ( {{- \Lambda}/120} )}*{\sum\limits_{j}p_{j}}}} & (8)\end{matrix}$

We can summarize this scoring procedure for event #4 in the table shownin FIG. 11C where the score for the PP raypath interpretation is 10.43which is much higher than the corresponding score of 1.77 for the SSraypath interpretation. The highest scoring ray path type interpretationis PP and is declared to be the ray path type of the detected arrivalevent. This score is similarly used to rank all the detected arrivalevents.

A summary of the processing steps and intermediate outputs for theautomated ray tracing inversion and 3D STC procedure may be seen in theflowchart displayed in FIGS. 11D1-11D2, which is executed for a numberof possible raypath types for each detected arrival event. The procedurebegins with the time pick of a detected arrival event (block 1151) asproduced by the automated time pick procedure, a number of possible raypath types (block 1153), the average P and S formation slownesses alongthe time pick (block 1155) as determined from standard acoustic P&Slogging at the wellsite, and the filtered waveform data corresponding tothe time pick (block 1157). The possible ray path types of block 1153may include PP or SS reflections or SP or PS mode converted refractions.The ray tracing inversion of block 1159 inverts the time pick for eachof the prescribed ray path types of block 1153 using the P & S formationslownesses (block 1155) to determine the following:

-   -   2D position and relative dip of the reflector relative to the        well track for one or more of the possible ray path types of        block 1153; such 2D reflector positions and relative dip for the        one or more possible raypath types is labeled as block 1161 in        FIGS. 11D1-11D2; the 2D reflector position and relative dip for        a given raypath type can be determined by varying the 2D        position of the reflector (and possibly varying relative dip at        each 2D position) and determining a predicted time pick at each        varying reflector position (and possibly at each relative dip)        based on the total travel times between the transmitter,        reflector and receiver, and finding the 2D position and relative        dip of the reflector where the predicted travel times most        closely match the detected time pick of block 1151 using the        relative error met using the relative error metric of Equation        (4); the predicted travel times are based on the average        acoustic formation slowness (or velocity) between the        transmitter and reflector and between the reflector and receiver        sensor for the given raypath type (from block 1155); the total        travel times can be computed between various depth locations of        source and 2D reflector position as shown in the black stars in        FIG. 8A and FIG. 9A and as in columns 5-6 and 7-8 of the table        in FIGS. 8H1-8H2);    -   a predicted time pick for one or more possible raypath types of        block 1153; such predicted time pick for the one or more        possible raypath types is labeled as block 1163 in FIGS.        11D1-11D2; for a given raypath type, the predicted time pick        corresponds to the 2D position and relative dip of the reflector        where the predicted time pick most closely matches the detected        time pick of block 1151 as solved by the ray tracing inversion;    -   a range of predicted inclination angles for each measured depth        station along the time pick for one or more of the possible        raypath types of block 1153; such range of predicted inclination        angles is labeled as block 1165 in FIGS. 11D1-11D2; an example        of such a range of predicted inclination angles are indicated in        the lower right corner of FIGS. 8A and 9A).        The time pick (block 1151) and the predicted time picks for a        number of possible ray-path types (block 1163) can be compared        using the relative error metric in Equation 5 which contributes        to the factors A (block 1167). The factors A (block 1167) can be        used in the scoring function of Equation 8 (block 1177) as is        seen in the first row of the table in FIG. 11C.

In FIGS. 11D1-11D2, 3D STC computations (block 1169) are performed ateach measurement station along the time pick (block 1151) for one ormore possible raypath types. In embodiments, the 3D STC operations canbe performed for each ray path type specified in 1153 irrespective ofthe value of the relative error ∇=∇({circumflex over (α)}, {circumflexover (d)}), that is, irrespective of how well the predicted travel timesmatch the detected time pick. However, note that if the relative erroris large, the corresponding score in Equation 8 will be low for this raypath type. As part of the 3D STC computations, 3D slowness can beparameterized in spherical coordinates, so that the 3D STC coherencefunction is given by coh(τ, s_(r), θ, φ). This parameterization requiresthe arrival time τ from the time pick, the formation slowness s_(r)along the time pick that corresponds to the raypath type (i.e., if “XY”represents the raypath type, we require the Y formation slowness fors_(r)), the range of inclination angles predicted by the ray tracinginversion (block 1165), and the full range of azimuth angles [−180°,180°] since the ray tracing inversion does not provide any informationpertaining to reflector azimuth φ. Note that if θ* is the inclinationangle predicted by the ray tracing inversion, then [θ*−25°, θ*+25° ] isused for our range of inclination angles.

In Block 1169 of FIGS. 11D1-11D2, 2D slices of the 3D STC representationof the waveforms collected at each measurement station along thedetected time pick are computed for each possible ray path type. Forexample, the 2D slice of the 3D STC representation for the PP raypathtype of the waveforms collected at the seventeenth position along thetime pick specified by the first two columns of the table in FIGS.8H1-8H2 is shown in FIG. 8B. This table entry row shows the toolmeasured depth location is X256.0 and corresponding reflection eventarrival time τ=4.9739 ms. The waveforms collected by the receiver sensorwith source-receiver offset z_(j) and receiver azimuth α_(k) at measureddepth X256.0 can be denoted w_(j,α) _(k) (t). The inclination anglepredicted for the PP ray path type at measured depth X256.0 is θ*=88° asshown in FIG. 8A. The P formation slowness at measured depth X256 iss_(r)=75 us/ft. To construct an appropriate 2D slice of the 3D STCrepresentation of the waveforms w_(j,α) _(k) (t), we allocate a 2D arraywhose 25 rows are indexed by the integer p=1, . . . , 25 and correspondto the values inclination angle given by θ_(p)=θ*+(p−12)*Δθ where Δθ=2°and whose 60 columns are indexed by the integer q=1, . . . , 60 andcorrespond to azimuth angles given by φ_(q)=(q−30)*Δφ where Δφ=6°. Thus,for the (p,q)^(th) entry of our matrix, we perform the 3D STCcomputation specified by coh(τ=4.9739, s_(r)=75 us/ft, θ_(p), φ_(q)) forthe waveforms w_(j,α) _(k) (t) collected at the tool measured depthposition X256.0 ft. The peak 3D STC value marked by the white ‘*’ inpanel 803 of FIG. 8B is determined by searching for the maximum value inthis 25-by-60 matrix.

The results of the 3D STC computations include 3D STC peak coherencevalues {p_(j)} as a function measured depth along the time pick for oneor more different raypath types (block 1171) and possibly a scatterplotof the 3D STC peak coherence values as a function of measured depthalong the time pick for one or more the different raypath types (block1173). The scatterplot shows the peak location coordinates specified interms of inclination and azimuth angles, such as the white ‘*’ in 803 ofFIG. 8B and the squares in 1003 of FIG. 10B and 1103 of FIG. 11B.Specifically, a peak value and peak location can be extracted from each2D slice of the 3D STC representation of the waveforms acquired at agiven measured depth. This results in a set of peak 3D STC values (1171)and peak locations for each ray path type. The peak locations are usedto form the scatterplot (1173), and the sum of the peak values is usedin 1177 below. Examples of such results are shown in the scatterplots ofFIGS. 8B, 9B, 10B and 11B from which can be observed both the locationsof the respective 3D STC coherence peaks as well as correspondingvalues. Angular dispersion of the 3D STC peak locations in both theazimuth and inclination angle directions (σ_(φ) and σ_(θ), respectively)can be derived for one or more possible raypath types in block 1175.

In block 1177, scores for a number of possible raypath types can becomputed, for example, using Equation 8 above. Note that the factor A(block 1167), the 3D STC peak value (block 1171) and the angulardispersion of the 3D STC peak location (block 1175) for a given ray pathtype contributes to the score in Equation 8 for the given ray path typeas seen in the last row of the table shown in FIG. 11C.

In block 1179, the scores for the number of possible raypath types ofblock 1177 can be evaluated or ranked to identify the raypath type withthe highest score, and the highest ranked raypath type is declared ordetermined to be the raypath type for the time pick and thecorresponding arrival event and reflector.

In some cases, only one raypath type can be considered by the raytracing inversion and processed by the 3D STC. For example, whenperforming sonic imaging using a dipole source, the ray tracinginversion and 3D STC processing can possibly only consider the SSraypath type under the assumption that only SS reflections will arisefrom formation fractures and faults. In this case, the scoringoperations of blocks 1177 can be avoided and block 1179 can declare theone raypath type be the raypath type for the time pick and thecorresponding arrival event and reflector.

In block 1181, a mean azimuth φ computed from the 3D STC peakscatterplot of the raypath type determined in block 1179 can be equatedto the value of the reflector azimuth φ relative to the acoustic loggingtool sonde. Details of an exemplary reflector azimuth angle φ andinclination angle θ relative to the acoustic logging tool sonde is shownin FIG. 8C.

In block 1183, the automated ray tracing and 3D STC procedure determinesthe 3D position and orientation of the reflector relative to the axis ofthe acoustic logging tool sonde in terms of the reflector azimuth φ(block 1181) and the two-dimensional reflector position relative to toolsonde as solved by the ray tracing inversion (block 1161) for theraypath type determined in block 1179. In this embodiment, the 3Dposition of the reflector relative to the tool sonde can beparameterized by a center point of the reflector defined by dimensionsof measured depth MD, radial offset r from the axis of the tool sonde,and the azimuth angle φ around the circumference of the tool sonde andborehole as shown in FIG. 8C. Furthermore, the orientation of thereflector relative to the tool sonde can be parameterized by a relativedip angle β and an inclination angle θ of the reflector as shown in FIG.8C. In this case, where the 2D reflector position relative to tool sonde(block 1161) is provided by a point P, an offset d and inclination angleα, such 2D parameters can be translated to dimensions of measured depthMD and radial offset r as part of the 3D position of the reflectorrelative to the tool sonde as follows. For 0<=α (in degrees)<=90,MD=MD(for point P)−d*cos(α) and r=d*sin (alpha); and for 90<α (indegrees)<=180, MD=MD(for point P)+d*cos(180−α) and r=d*sin (180−alpha).

The determination of the 3D position of the reflector relative to theaxis of the acoustic logging tool sonde can be satisfactory for verticalwells. However, for many applications, it is useful to determine the 3Dposition and orientation of the reflector in a true (or real-world) 3Dspace (without reference to the well trajectory) in block 1185. Inembodiments, the true 3D position of the reflector can be parameterizedby a center point of the reflector defined in a geographical 3Dcoordinate system (e.g., latitude/longitude/height with respect to thereference ellipsoid surface of the earth), a geocentric coordinatesystem (e.g., the Earth-Centered Earth-Fixed coordinate system, avertical earth coordinate system (e.g., elevation or depth with regardto the geoid), or other suitable 3D earth coordinate system. The true 3Dorientation of the reflector can be parameterized by coordinates N_(x),N_(y), N_(z) of a normal vector that extends from the reflector at itscenter point. The coordinates N_(x), N_(y), N_(z) of the normal vectorfor the reflector can be translated to a true dip angle Θ and trueazimuth angle Φ for the reflector as shown in FIGS. 8F and 8G.

In embodiments, the 3D position and orientation of a reflector true (orreal-world) 3D space can be determined by defining points A, B, C, and Dare defined in the plane of the reflector at a fixed offset (the defaultsetting is 5 feet) from the centerline of the reflector marked as thedashed line in FIG. 8C. Using well survey information (block 1187),which can include hole inclination and azimuth information, one canderive a series of three linear rotations and a translation to computethe 3D positions of the points T(A), T(B), T(C), and T(D) and the actualwell trajectory as illustrated in the FIG. 8F. One can then compute thenormal vector (N_(x), N_(y), N_(z)) to the surface by computing thecross product of the vectors {right arrow over (T(A)T(C))} and either{right arrow over (T(B)T(D))} or {right arrow over (T(D)T(B))} so thatN_(z)>0. From the normal vector (N_(x),N_(y), N_(z)), one can derive thereflector true dip Θ and true azimuth Φ via the Equations 9 and 10 asfollows:

Θ=arccos(N _(z),√{square root over (N _(x) ² +N _(y) ²)}).  (9)

Φ=arctan(N _(x) ,N _(y)).  (10)

In one embodiment, the 3D STC computations involve processing waveformsw_(j,α) _(k) recorded by acoustic receivers located at positions aroundthe circumference of a sonic tool with coordinates

(x _(j,α) _(k) =r _(tool) cos α_(k) ,r _(tool) sin α_(k) ,z _(j) −z ₁).

The coherent energy for a time window of duration T_(W) starting at timeτ and slowness vector s=(s_(x), s_(y), s_(z)) in 3D Cartesiancoordinates with z being the direction along the axis of the tool sondeis given by:

E _(c)(τ,s)=∫₀ ^(T) ^(W) [Σ_(j,k) w _(j,α) _(k) (t−τ−s·x _(j,α) _(k) )]²dt  (12)

The use of the inner product s·x_(j,α) _(k) in Equation (12) means thatthe waveforms w_(j,α) _(k) are being stacked with a linear moveoutcorresponding to the source-receiver offset according tos_(z)*(z_(j)−z₁) and with a sinusoidal moveout corresponding to thereceiver azimuth according to r_(tool)*(s_(x) cos α_(k)+s_(y) sinα_(k)).

The corresponding incoherent energy and coherence arrays are thendefined using

$\begin{matrix}{{E_{i}( {\tau,s} )} = {\sum_{j,k}{\int_{0}^{T_{w}}{{w_{j,\alpha_{k}}^{2}( {t - \tau - {s \cdot x_{j,\alpha_{k}}}} )}{dt}}}}} & (13) \\{{{coh}( {\tau,s} )} = \sqrt{\frac{E_{c}( {\tau,s} )}{M{\Omega }{E_{i}( {\tau,s} )}}}} & (14)\end{matrix}$

Here Ω represents the set of the receiver sensor angles α_(k), and M isthe number of receivers in the receiver array.

When using a dipole or quadrupole source, one must observe that thepolarity of the wave reflected from a nearby boundary is a function ofthe source polarity. To process acoustic waveform data generated by adipole source at multiple source azimuths using the 3D STC, we must adda sign term sgn(cos(φ_(s)−β)) that accounts for the polarity of thedipole source with azimuth β relative to the stacking azimuth, φ_(s).This modification allows the reflected and refracted arrivals to bestacked in a coherent manner.

$\begin{matrix}{{E_{c}( {\tau,s} )} = {\int_{0}^{T_{w}}{\lbrack {\underset{{offset}\mspace{14mu} j}{\sum_{{({\alpha,\beta})} \in \Omega}}{,{{{sgn}( {\cos ( {m( {\phi_{s} - \beta} )} )} )}{w_{j,\alpha,\beta}( {t - \tau - {s \cdot x_{j,\alpha}}} )}}}} \rbrack^{2}{{dt}.}}}} & (15)\end{matrix}$

Here m is the number of poles in the multipole source (monopole m=0,dipole m=1, etc.).

Note that the signum function is not a function of the receiver azimuthα but rather a function of the source azimuth β, so the direct arrivalsremain largely incoherent in this 3D STC representation. Thecorresponding incoherent energy E_(i)(τ,s) and coherence coh(τ,s)functions are defined for multipole sources in an entirely analogousfashion.

$\begin{matrix}{{{E_{i}( {\tau,s} )} = {\sum_{j,k}{\int_{0}^{T_{w}}{{w_{j,\alpha,\beta}^{2}( {t - \tau - {s \cdot x_{j,\alpha_{k}}}} )}{dt}}}}},} & (16) \\{{{coh}( {\tau,s} )} = {\sqrt{\frac{E_{c}( {\tau,s} )}{M{\Omega }{E_{i}( {\tau,s} )}}}.}} & (17)\end{matrix}$

Here Ω now represents the set of the source (β) and receiver sensor (α)angles.

The 3D STC coherence functions coh(τ, s) described Equations 14 and 17are parameterized here using an arrival time T and Cartesian slownessvector s=(s_(x), s_(y), s_(z)), whereas the function used for the 3D STCcomputations described above uses a function coh(τ, s_(r), θ, φ) whichis the same function but parameterized in spherical coordinates.Specifically, for values (s_(r), θ, φ) the corresponding s_(x), s_(y),s_(z) values are given by:

s _(x) =s _(r) sin θ cos φ  (18)

s _(y) =s _(r) sin θ sin φ  (19)

s _(z) =s _(r) cos θ  (20)

Note that the formation slowness s_(r) corresponds to the propagationmode (or raypath type) under consideration. Thus, if “XY” represents thepropagation mode (or raypath type) under consideration, then the Yformation slowness is used for s_(r). For example, if the PP raypathtype is under consideration, then the P-type formation slowness is usedfor s_(r).

The information derived from this second automated procedure can besummarized in a variety of ways.

Example graphical user interfaces are illustrated in FIGS. 12A-12D,which includes a panel 1201 of FIG. 12A that shows the filtered waveformdata recorded on the 3rd receiver sensor in the 7th ring of receivers.The overlay indicates the position of several arrival events whose scoreis at least 2.24. Each events can be labeled with an index value thatuniquely identifies the event from the other events. For example Event#4, which is labeled by “Index” as shown, is a PP reflection and hasbeen selected from the waveform viewer. Its corresponding ray tracinginversion result is shown in panel 1207 of FIG. 12B, while panel 1209 ofFIG. 12C shows the 3D STC analysis at the measured depth stationindicated by the dark ray path in panel 1207 and by the two horizontalblack time gates along Event #4 in the waveform viewer panel 1201 ofFIG. 12A.

Note that in FIG. 12A, the waveform viewer panel 1201 can have anoverlay of the arrival events color-coded according to their ray pathtype. The hue of the boxes that describe each arrival event can bedetermined according to the highest scoring ray path type. Cyan colorrepresents the ‘PP reflected’ ray path type; ‘Brown’ represents ‘SPrefracted’; ‘Magenta’ represents ‘PS refracted’; while ‘Orange’represents ‘SS reflected’. Optionally, the intensity of the color can bedetermined according to the scoring rank or relative score derived fromEquation 8. In addition, a log panel 1203 of FIG. 12D depicts the raypath type, receiver index, and measured depth of the arrival eventsusing the same color scheme, which is similar to FIG. 3B.

FIG. 13 shows the common receiver azimuth view of the filtered waveformdata (receiver index #1) with highlighted event #8. FIG. 14 shows thecommon ring view of the filtered waveform data (ring #7) withhighlighted event #8. Note that the moveouts of the arrival events shownin FIGS. 13 and 14 are determined from the time pick and the estimatedvalues for event #8's 3D STC azimuth and inclination angles.

Several different log panels can be used to show different attributes ofthe arrival events and corresponding near wellbore formation structures.These include the panels of FIG. 3C and FIG. 4D where the azimuth of thereflectors is shown where said azimuth is determined by the mean azimuthof the 3D STC peaks shown in FIGS. 10B and 11B. The horizontal positionof these boxes can be determined by other parameters derived by theproposed workflow. For example, we could choose to use the meaninclination of the 3D STC peaks shown in FIGS. 10B and 11B or to use theestimated relative reflector dip derived using the ray tracing inversionshown in the panels 801 and 901 of FIGS. 8A and 9A and shown in the logpanel 1207 of FIG. 12B.

All of these log displays give the user a quick assessment of thepossible location and characteristics of the various arrival events thatmay be present in the filtered waveform measurements. These color codingshows the various ray path types and the relative prominence of thecorresponding arrival events, while the spatial positions of the boxesin the display give a valuable summary view of the spatial location ofthe corresponding reflector.

In addition to using the waveform viewer and log panels to depict thearrival events and their attributes, we can summarize the structural(reflector) information derived from the automated procedures using thedisplay maps of the reflectors as shown in FIGS. 4A to 4C. The displaymap of the reflectors shown in panel 401 of FIG. 4A shows the threedimensional positions of the reflectors relative to the tool axis (welltrack), which are obtained using the reflector positions determined bythe ray tracing inversions (as depicted in the panels 801 and 901 ofFIGS. 8A and 9A) and mean 3D STC azimuths (as depicted in the panels1003 and 1103 of FIGS. 10B and 11B). The color-coding of the reflectorsallows the user to observe the mode of the ray path that is used toimage the reflectors. The display map of the reflectors shown in panel405 of FIG. 4C illustrates the two-dimensional position (e.g., measureddepth and azimuth) for the reflectors within a user-specified azimuthalrange or window. More particularly, the user specified azimuthal windowshown in the panel 405 includes two parts which are offset from oneanother by 180° of azimuth. One part is defined by a pair of solidvertical line segments, and the other part is defined by a pair ofdashed vertical line segments. The width or size of each part (indegrees of azimuth) is a user defined parameter. Furthermore, the usercan adjust the center positions for the two parts (which are offset fromone another by 180° of azimuth) using the slider bar 406 in FIG. 4C. Thereflectors positioned with this two-part user-specified azimuthal rangeor window are displayed as part of the display map of panel 401 as shownin FIG. 4A. The first slider bar 407 (from the left-hand side) in FIG.4A determines the start of the measured depth interval of the arrivalevents used for the display maps, while the slider bar 408 determinesthe lower bound on the scores of the arrival events used for the displaymaps. These display maps provide the user with a quick summary of thespatial positions of the near wellbore formation structures withouthaving to migrate the waveform measurements.

While we have emphasized the automatic nature of this ray tracing and 3DSTC-based portion of the workflow, the user is able to assess thequality of the interpretation and modify (or tune) the parameters of theprocedure if desired. In the waveform panel 1201 of FIG. 12A, the userhas manually selected event #4 and selected one of the menu items toassess the quality of the imaging results for that event. The panels1207 and 1209 of FIGS. 12B and 12C, respectively, show the ray tracinginversion result and 3D STC slice for the measurement stationhighlighted by the line segments in the ray tracing diagram and by thetwo horizontal black line segments along the time pick associated withthe label “INDEX” as shown in the waveform display 1201 of FIG. 12A. Theuser can shift the measurement station along the time pick to examinethe 3D STC slices as well as review the graphs of the ray tracingresults fit to the data and 3D STC peak histograms as shown in FIGS. 10Band 11B. The user can examine all of these results for different choicesof ray path type by user input, as well as manually promote/demote therankings of the ray path types for the selected arrival event by userinput. This facility gives the user a very important means of examiningand modifying the details of this portion of the automated workflow.

Automated Migration Configuration Procedure

As we mentioned earlier, the task of configuring the migration requiresdetermining several important parameters: (a) the ray path type of thearrival events of interest; (b) the azimuth of the arrival events; and(c) the migration aperture. In addition, to reduce the number ofartifacts in the migration imager, many users will choose to impose aconstraint on (d) the relative dip of the reflectors. We note that allof these parameters have been determined for the each of the arrivalevents processed by the first two stages of our automated workflow. Toallow the user to configure the migration in an automated fashion, we(1) group the arrival events according to proximity and ray path type;(2) allow the user to review the migration parameters that have beenautomatically prepared for each group, for example as shown in FIG. 15;and then (3) allow the user to select the arrival events that will bemigrated.

Very frequently there are many arrival events associated with the samereflected wavefront received by the acoustic sensors of the downholesonic tool. For example, in the waveform panel 1201 of FIG. 12A, weobserve that certain events near 4 msec and between X260 and X220 inmeasures depth very clearly belong to the same wavefront arrival. Inaddition, since the same wavefront arrival event is very often observedat different receiver sensors around the circumference of the toolsonde, additional arrival events also will correspond to the samewavefront arrival event. To help organize the task of configuring themigration, the arrival events are grouped according to proximity inmeasured depth and according to ray path type. Two arrival events areconsidered close in measured depth if they are closer than theircorresponding migration apertures.

The results of grouping the arrival events in this way can be seen inthe panel of FIG. 3D where vertical blue bars highlight four groups ofarrival events. The length of these blue bars includes the arrivalevents themselves plus the largest migration aperture found among thegroup. To review the migration parameters that are automaticallygenerated for a candidate migration, the user can select thecorresponding blue bar. A dialogue box such as what appears in FIG. 15allows the user to review the measured depth of the waveformmeasurements used for the migration, a relative dip constraint for themigration which the user may wish to impose, a suggestion for muting thewaveforms used in the migration based on the time window selected toconfigure the automatic time picking procedure, options for generating a2D migration images normal to or along the well track, the size of thecanvas used for the migration, the pixel resolution of the migration, aswell as options for weighting the migration according to the 3D STCcoherence of the arrival event and/or according to a more standardgeometric spreading.

Finally, the user can select a group of migrations that will contributeto the final migration image.

Migration and Results OC

The migration procedure performs the migration, adds together thecontributions from each of the migration groups defined in the previoussection and provides the migration result along with map of reflectorsthat is used to QC the migration result. The results of this procedureare shown in panel 403 of FIG. 4B. As with the generation of the displaymap of the reflectors shown in panel 403 of FIG. 4B, the ray tracinginversion and mean 3D STC azimuth estimate can be sed to determine theposition of the QC reflectors in the migration image. Many users gainconfidence in the results from seeing these QC reflectors. These QCreflectors help to avoid becoming confused by artifacts that can appearin sonic imaging migration results. Again, the labels of indices of theQC events can correspond to the labels or indices of the arrival eventsshown in the waveform viewer panel 1201 of FIG. 12A, so that the usercan indeed associate particular features in the migration image withparticular waveform features.

Output of Mapped Reflectors for Downstream Workflows

While we have demonstrated that we can visualize the mapped reflectors(near wellbore formation structures) in three-dimensions andtwo-dimensions as seen in panels 401, 403 and 405 of FIGS. 4A, 4B, and4C for purposes of confirming migration results and furtherunderstanding the sonic imaging response, these mapped reflectors can beof significant independent interest for processing with severaldownstream applications. Thus, it is often very helpful to output thesemapped reflectors in the form of a spreadsheet or other structured dataobject that contains information that describes the 3D position andorientation as well as ray path type of each reflector. An examplespreadsheet is displayed in FIGS. 16A1-16A3 where each row ofcorresponds to a particular reflector and includes an identifier (ID),ray path type, measured depth intercept, relative dip β, azimuth φ, truedip Θ and true azimuth Φ, and information related to the spatial offsetof the reflector relative to the tool axis (or well track). The rows ofthe spreadsheet follow the event color coding used in the viewer logs asnoted by the row color scheme defined adjacent the last column in FIG.16A3. In the scheme, reflectors with PP ray path types are highlightedin cyan, reflectors with SS ray path types are highlighted in orange,reflectors with SP refracted ray path types are highlighted in brown,and reflectors with PS refracted ray types are highlighted in magenta.

In the spreadsheet of FIGS. 16A1-16A3, the first column shows the indexor identifier (ID) that corresponds to indices used for events followingautomated ray tracing and 3D STC procedure. The second and third columnsindicate ray path type of the reflectors. The last three columns andcolumns K and L provide information that can be used to specify thethree-dimensional position and orientation of each reflector relative tothe tool axis (well track).

Note that the reflectors with SP refracted ray path types that arehighlighted in brown as well as the reflectors with PS refracted raytypes are highlighted in magenta are typically indicative of particularformation features of interest (such as fractures, bed boundaries orfaults).

The workflow can generate and display other views that displayinformation that characterizes the orientation of the reflectors as afunction of depth along the well track. Such other views can include atadpole plot as shown in FIG. 16B where measured depth is plotted alongthe y axis, the magnitude of reflector's true dip is plotted along the xaxis, and the direction of the tadpole tail indicates the true azimuthof the reflector. The position of the points along the x axis in thetadpole plot convey the magnitude of true dip Θ for the reflectors as afunction of measured depth, while the direction of the tadpole tailconveys the true azimuth Φ for the reflectors relative to True North.

Such other views can also include views that display the informationthat characterizes the orientation of the reflectors without referenceto depth along the well track such as an upper hemisphere stereonet plotas shown in FIG. 16C. Inside the circle shown in FIG. 16C, the radialpositions of the points convey the true dip directions Θ for thereflectors, while the angular positions of the points convey the trueazimuth Φ directions for the reflectors.

Such other views can also include a Rose diagram as shown in FIG. 16D,which shows frequency of true azimuth directions Φ of the reflectorsinside a circle.

One or more these other views can be displayed in conjunction with otherlogs, such as an FMI image of a corresponding interval of measured depthor possibly a tadpole diagram, stereonet plot or Rose diagram of truedip and azimuth that is derived from the FMI image. In this case, theFMI image is derived from resistivity measurements of the formation asshown in FIGS. 16E, 16F and 16G. When a fracture or bedding planeintersects the borehole as in FIG. 16E, the FMI image will show acorresponding change in resistivity along a sinusoidal curve as seen inthe features of FIG. 16F. The phase and amplitude of these detectedsinusoidal feature in the FMI image can be used to calculate the truedip and azimuth of the corresponding fracture plane. This calculatedtrue dip and azimuth can then be displayed in a corresponding tadpolediagram, stereonet plot or Rose diagram.

FIG. 17 is an example flow diagram that illustrates the operations ofthe improved workflow described above. Note that elements 1707, 1709,1711, 1713, 1715 and 1717 represent novel workflow elements compared tocurrent state of the art processing. The operations of FIG. 17 may beperformed using a processor, a controller and/or any other suitableprocessing device, such as a computer processing system described belowwith respect to FIG. 22. In embodiments, the computer processing systemcan be located on the surface at the wellsite (or at a remote location)and coupled to the processing system of the downhole sonic tool thatrecords the sonic waveform data obtained by the downhole sonic tool by asuitable telemetry subsystem. Other system architectures can also beused.

In block 1701, the downhole sonic tool is operated in a wellbore totransmit sonic signals that probe or interact with near wellboreformation structures and receive sonic waveform measurements over aninterval of measured depths in the wellbore. The resulting sonicwaveform data is processed to obtain filtered sonic waveform data (block1703) as well as compressional (P) and shear (S) slowness logs (block1705).

In block 1707, the filtered sonic waveform data (block 1703) and thecompressional (P) and shear (S) slowness logs (block 1705) are used aspart of the automatic time pick and event localization procedure asdescribed herein.

In block 1709, the results of the automatic time pick and eventlocalization procedure are used as part of the ray tracing and 3D STCprocessing as described herein, which can produce arrival event logs,display maps of reflectors and a spreadsheet (or other structure dataobject(s) of reflector information (block 1711).

In block 1713, parameters are determined for configuring the migrationin an automated fashion. This involves grouping arrival events accordingto proximity and ray path type, allowing the user to review themigration parameters that have been automatically prepared for eachgroup, and allowing the user to select the arrival events that will bemigrated.

In block 1715, the migration is performed based on the parametersdetermined in 1713 and the image resulting from the migration isdisplayed for viewing. The displayed image includes QC marking for thereflectors as described above.

In block 1717, the information contained in the display maps ofreflectors or the spreadsheet (or other structured data object(s)) ofreflector information of block 1711 can be used to supplement areservoir model for reservoir analysis, visualization and/or simulation.The reservoir model can be based on surface seismic measurements, VSPmeasurements, LWD electromagnetics measurements and possibly othermeasurements as well. In particular, the three-dimensional position andorientation of a particular reflector relative to the tool axis (or welltrack) can be used to supplement the information of the reservoir modelthat corresponds to the three-dimensional position and orientation ofthat reflector. In this manner, the improved reservoir model can besubject to reservoir analysis, visualization and/or simulationoperations, which are typically performed to gain a better understandingof the subsurface formation structures that leads to informed wellplacement, reserves estimation and production planning. The supplementalinformation of the reservoir model related to the reflector informationcan provide valuable information that confirms or otherwise identifiesthe possibility of formation structures of interest (such as fractures,bed boundaries and faults). The knowledge and understanding of thelocation and orientation of these formation structures can be animportant part in making well informed decisions with regard to wellplacement, reserves estimation, production planning, and possibly otherapplications as well.

Note that in embodiments the workflow can derive the display maps ofreflectors or the spreadsheet (or other structured data object) ofreflector information (blocks 1701-1711) and use such information tosupplement the reservoir model for reservoir analysis, visualizationand/or simulation (block 1717). In this case, the operations of block1713 and 1715 of the flow diagram of FIG. 17 can be omitted from theworkflow or bypassed as part of the workflow.

Also note that operation of blocks 1701-1711 can accurately characterizereflected arrival events with the different ray path types (PP, SP, SP,SS) and map the corresponding reflectors without resorting to any kindof migration (which is really not feasible or practical downhole). Inaddition, the reflector information collected in block 1711 is a verycompact representation of the reflectors along the well trajectory.

Note that blocks 1701-1711 can be part of operations outlined by thedotted line 1719 that can take place downhole in the tool sonde. Inembodiments, the processing (filtering) of the sonic waveformmeasurements that generate the filtered sonic waveform data of block1703 can be configured to be a median filter. The automatic time pickprocedure of block 1707 can be configured to look for events comingbefore direct shear wavefronts, and the automatic ray tracing and 3D STCprocessing of block 1709 can be configured to look for PP, PS, and SPtype mode converted arrival events. The operations of blocks 1701-1711can be carried out on a buffered collection of sonic waveforms acquiredduring the most recent interval of measured depths (such as the mostrecent 100-200 feet of measured depth). The reflector informationcollected in block 1711 can be organized in decreasing order of theirscore according to Equation 8 above. In this case, depending on thebandwidth of the telemetry system between the downhole tool sonde andthe surface-located processing system, the reflector information for asubset of the top-ranked reflectors (for example, the top ranked 10-20reflectors) can be communicated to the surface-located processing systemfor storage and/or output and use in the reservoir analysis,visualization and/or simulation of block 1717.

Note that in logging-while drilling applications, the downhole sonictool rotates during acquisition of the sonic waveforms and often employsa dipole or multipole acoustic transmitter. Independent of thetransmitter type, the acquired sonic waveforms can be organizedaccording to source-receiver offset and actual receiver azimuth (insteadof organized according to source-receiver offset and nominal receiverindex used for wireline applications). That is, at each measured depthposition, the acquired sonic waveforms can be binned according to theazimuth of the tool receiver. Furthermore, when using a dipole ormultipole transmitter, the binning procedure can be adapted to accountfor the polarity of the transmitter. This arises because the polarity ofthe reflected wave depends upon whether the positive or negative pole ofthe transmitter is facing the reflector as the tool rotates. Inaddition, the polarity of the borehole modes is also a function of thepolarity of the transmitter as illustrated in FIGS. 18A and 18B. Inorder to remove the signature of the polarity of the transmitter fromthe binned sonic waveforms, the waveform data that goes into each bincan be multiplied according to the formula shown in FIG. 18A. In effect,this reverses the effects of the negative polarity. Note that withoutthis compensation, the median filtering performed on the binned waveformdata to remove the borehole modes will be ineffective, because theborehole modes will be multiplied by a possibly highly oscillatorypolarity function of the transmitter as a function of measured depth. Inaddition, the same will be true of the underlying reflected signals, sothat the automatic time pick procedure (block 1707) will be ineffective.In logging-while drilling applications, the reflector information can beused to generate and display a real time mapping of fractures, faults,bed boundaries, and other acoustic reflectors relative to the welltrajectory while drilling. Such real-time mapping can be used todynamically adjust the trajectory of the drilling operation (or stop thedrilling operation) as deemed optimal for the geometry of the formationstructures with knowledge of the location of the fractures, faults, bedboundaries, and other acoustic reflectors.

Additional Field Data Example

In a field data example shown in FIGS. 19A-19D, fractures near the welltrack with events give rise to arrival events with different ray pathtypes. The waveform viewer panel 1901 of FIG. 19A shows the filteredwaveform data with overlay of four reflectors characterized by automatedray tracing and 3D STC procedure. The panels 1907 and 1909 of FIGS. 19Band 19C, respectively, allow for quality control for a certain arrivalevent. Panel 1907 of FIG. 19B shows ray tracing inversion with the SPray path model, while panel 1909 of FIG. 19C shows 3D STC analysis atmeasured depth station indicated by dark ray path in panel 1907 of FIG.19B. The event log viewer panel 1903 of FIG. 19D gives mapping ofrelative reflector dip versus measured depth for all events with scoresgreater than 2.05.

The waveform viewer panel 1901 of FIG. 19A shows arrival events andreflectors as a function of measured depth (x axis) and time (y axis).The event log viewer 1903 of FIG. 19D shows the reflectors as a functionof dip angle (x axis) and measured depth (y axis). The waveform viewerpanel 1901 of FIG. 19A and the event log viewer 1903 of FIG. 19D can usethe same color-coding as the spreadsheet of FIGS. 16A1-16A3 where cyancorresponds to PP reflectors, brown corresponds to SP reflectors, andmagenta corresponds to PS reflectors. Note that the presence of a PSreflector (magenta-colored) crossing an SP reflector (brown-colored) aspart of the waveform viewer panel 1901 of FIG. 19A, which is shown inthe depth interval between X670 and X660 in FIG. 19A, is typicallyindicative of formation features of interest (such as fractures, bedboundaries or faults). The two panels 1907 and 1909 of FIGS. 19B and19C, respectively, can be used to access the quality of theinterpretation of the SP mode converted arrival around measured depthX663 ft. The event log viewer 1903 of FIG. 19D indicates the SP and PSmode converted arrivals seem to be separated in azimuth by about 90°instead of 180°, as we might otherwise expect from visually inspectingthe waveforms.

Similar to FIGS. 4A-4C, the panels of FIGS. 20A-20F summarize thestructural information derived from the automated procedures using the2D and/or 3D mapping of the reflectors relative to the tool axis (orwell track). The 3D map of the reflectors shown in panel 2001 of FIG.20A and in the panel of FIG. 20F is obtained using the 3D position andorientations of the reflectors determined by the ray tracing inversionsand mean 3D STC azimuths together with the well survey information. Thecolor-coding of the reflectors allows the user to observe the mode ofthe ray path that is used to image the reflector. The 2D map of thereflectors shown in panel 2003 of FIG. 20B is shown for the arrivalevents selected in the panel 2005 of FIG. 20C by the two pairs of dashedand solid vertical pink line segments. Panel 2005 of FIG. 20C and thepanel of FIG. 20D show reflector azimuth φ as a function of measureddepth. In panel 2005 of FIG. 20C, the user selects the positions ofthese line segments using the slider bar 2006. The selected azimuthvalue determines the azimuth center of the two solid line segments, andthe azimuth position of the two dashed line segments offset by 180°. Theazimuth window size of the two pairs of line segments is a user definedparameter. The arrival events selected by the two solid line segmentsappear with positive lateral offset in the 2D display of panel 2003 ofFIG. 20B. The slider bar 2007 determines the start of the measured depthinterval of the arrival events used for the 3D and 2D displays of panels2001 and 2003 of FIGS. 20A and 20B, while the slider bar 2008 determinesthe lower bound on the scores of the arrival events used for the 3D and2D displays of panels 2001 and 2003 of FIGS. 20A and 20B. FIG. 20E is atadpole diagram representing the true dip and azimuth of the reflectorsas a function of measured depth. The 3D and 2D displays of panels 2001and 2003 of FIGS. 20A and 20B and the panel of FIG. 20F along with thetadpole log shown in FIG. 20E provide the user with a quick summary ofthe spatial positions of the reflectors without having to migrate thewaveform measurements.

FIGS. 20A-20F show the structural information derived for the field dataexample of FIGS. 19A-19D. Panel 2001 of FIG. 20A and the panel of FIG.20F shows 3D ray tracing for all events in green window selected inpanel 2005 of FIG. 20C, while panel 2003 of FIG. 20B shows 2D raytracing results that also fall within the azimuth sub-windows. Thelabels or indices of the reflectors can correspond to waveform arrivalevents labeled in FIGS. 19A-19C. We note, in particular, the modeconverted arrivals near measured depth (MD) X660 ft represent modeconverted arrivals possibly corresponding to fractures. We observe thatthe reflectors generating the PS and SP mode converted arrivals are notin the same 2D plane, possibly indicating two fractures intersecting at90° angles, as is clear from FIG. 20D. This orthogonal structure is alsoevident in (a) the tadpole log in FIG. 20E where the angular orientationof the tadpoles are perpendicular to one another and in (b) the visual3D representations of the fractures themselves in FIG. 20F and panel2001 of FIG. 20A. Mapping of near wellbore reflectors in 3D and derivingthis structural information without requiring a migration step is veryhelpful, particularly when the arrival events exhibit multiple raypathtypes and reflector azimuths, as is common for fracture imaging.

FIG. 21 illustrates a wellsite system in which the examples disclosedherein can be employed. The wellsite can be onshore or offshore. In thisexample system, a borehole 11 is formed in subsurface formations byrotary drilling. However, the examples described herein can also usedirectional drilling, as will be described hereinafter.

A drill string 12 is suspended within the borehole 11 and has a bottomhole assembly 100 that includes a drill bit 105 at its lower end. Thesurface system includes a platform and derrick assembly 10 positionedover the borehole 11. The assembly 10 includes a rotary table 16, akelly 17, a hook 18 and a rotary swivel 19. The drill string 12 isrotated by the rotary table 16. The rotatory table 16 may be energizedby a device or system not shown. The rotary table 16 may engage thekelly 17 at the upper end of the drill string 12. The drill string 12 issuspended from the hook 18, which is attached to a traveling block (alsonot shown). Additionally, the drill string 12 is positioned through thekelly 17 and the rotary swivel 19, which permits rotation of the drillstring 12 relative to the hook 18. Additionally or alternatively, a topdrive system may be used to impart rotation to the drill string 12.

In this example, the surface system further includes drilling fluid ormud 26 stored in a pit 27 formed at the well site. A pump 29 deliversthe drilling fluid 26 to the interior of the drill string 12 via a portin the swivel 19, causing the drilling fluid 26 to flow downwardlythrough the drill string 12 as indicated by the directional arrow 8. Thedrilling fluid 26 exits the drill string 12 via ports in the drill bit105, and then circulates upwardly through the annulus region between theoutside of the drill string 12 and the wall of the borehole 11, asindicated by the directional arrows 9. In this manner, the drillingfluid 26 lubricates the drill bit 105 and carries formation cuttings upto the surface as it is returned to the pit 27 for recirculation.

The bottom hole assembly 100 of the example illustrated in FIG. 28includes a logging-while-drilling (LWD) module 120, ameasuring-while-drilling (MWD) module 130, a roto-steerable system andmotor 150, and the drill bit 105.

The LWD module 120 may be housed in a special type of drill collar andcan contain one or more logging tools. In some examples, the bottom holeassembly 100 may include additional LWD and/or MWD modules. The LWDmodule 120 may include capabilities for measuring, processing, andstoring information, as well as for communicating with the surfaceequipment.

The MWD module 130 may also be housed in a drill collar and can containone or more devices for measuring characteristics of the drill string 12and/or the drill bit 105. The MWD tool 130 further may include anapparatus (not shown) for generating electrical power for at leastportions of the bottom hole assembly 100. The apparatus for generatingelectrical power may include a mud turbine generator powered by the flowof the drilling fluid. However, other power and/or battery systems maybe employed. In this example, the MWD module 130 includes one or more ofthe following types of measuring devices: a weight-on-bit measuringdevice, a torque measuring device, a vibration measuring device, a shockmeasuring device, a stick slip measuring device, a direction measuringdevice and/or an inclination measuring device.

Although the components of FIG. 21 are shown and described as beingimplemented in a particular conveyance type, the examples disclosedherein are not limited to a particular conveyance type but, instead, maybe implemented in connection with different conveyance types including,for example, coiled tubing, wireline wired drill pipe and/or any otherconveyance types known in the industry.

In embodiments, the LWD module 120 includes a sonic measuring device ortool that includes at least one acoustic source or transmitter spacedfrom an array of acoustic receivers or sensors. The at least oneacoustic transmitter can include a monopole transmitter and/or a dipoletransmitter and/or a multipole transmitter as is well known in the arts.The transmitter is operated to emit wavefronts that probe the nearwellbore structures (e.g., fractures, nearby bed boundaries and faults,etc.) by propagating, reflecting, and refracting across thesestructures. The acoustic receivers can cooperate with at least oneprocessing system (FIG. 22) of the tool to receive and record theresulting wavefronts in synchronization with the firing of the signal toobtain the sonic waveform data at varying depths in the borehole 11 asis well known. The recorded sonic waveform data at varying depths in theborehole can then be filtered and processed further for sonic imaging,for example by a surface-located data processing system, according tothe details of the improved workflow as described above.

FIG. 22 illustrates an example device 2500, with a processor 2502 andmemory 2504 that can be configured to implement various embodiments ofmethods as discussed in this disclosure. Memory 2504 can also host oneor more databases and can include one or more forms of volatile datastorage media such as random-access memory (RAM), and/or one or moreforms of nonvolatile storage media (such as read-only memory (ROM),flash memory, and so forth).

Device 2500 is one example of a computing device or programmable device,and is not intended to suggest any limitation as to scope of use orfunctionality of device 2500 and/or its possible architectures. Forexample, device 2500 can comprise one or more computing devices,programmable logic controllers (PLCs), etc.

Further, device 2500 should not be interpreted as having any dependencyrelating to one or a combination of components illustrated in device2500. For example, device 2500 may include one or more of a computer,such as a laptop computer, a desktop computer, a mainframe computer,etc., or any combination or accumulation thereof.

Device 2500 can also include a bus 2508 configured to allow variouscomponents and devices, such as processors 2502, memory 2504, and localdata storage 2510, among other components, to communicate with eachother.

Bus 2508 can include one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. Bus 2508 can also include wiredand/or wireless buses.

Local data storage 2510 can include fixed media (e.g., RAM, ROM, a fixedhard drive, etc.) as well as removable media (e.g., a flash memorydrive, a removable hard drive, optical disks, magnetic disks, and soforth).

One or more input/output (I/O) device(s) 2512 may also communicate via auser interface (UI) controller 2514, which may connect with I/Odevice(s) 2512 either directly or through bus 2508.

In one possible implementation, a network interface 2516 may communicateoutside of device 2500 via a connected network.

A media drive/interface 2518 can accept removable tangible media 2520,such as flash drives, optical disks, removable hard drives, softwareproducts, etc. In one possible implementation, logic, computinginstructions, and/or software programs comprising elements of module2506 may reside on removable media 2520 readable by mediadrive/interface 2518.

In one possible embodiment, input/output device(s) 2512 can allow a userto enter commands and information to device 2500, and also allowinformation to be presented to the user and/or other components ordevices. Examples of input device(s) 2512 include, for example, sensors,a keyboard, a cursor control device (e.g., a mouse), a microphone, ascanner, and any other input devices known in the art. Examples ofoutput devices include a display device (e.g., a monitor or projector),speakers, a printer, a network card, and so on.

Various processes of present disclosure may be described herein in thegeneral context of software or program modules, or the techniques andmodules may be implemented in pure computing hardware. Softwaregenerally includes routines, programs, objects, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. An implementation of these modules andtechniques may be stored on or transmitted across some form of tangiblecomputer-readable media. Computer-readable media can be any availabledata storage medium or media that is tangible and can be accessed by acomputing device. Computer readable media may thus comprise computerstorage media. “Computer storage media” designates tangible media, andincludes volatile and non-volatile, removable and non-removable tangiblemedia implemented for storage of information such as computer readableinstructions, data structures, program modules, or other data. Computerstorage media include, but are not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other tangiblemedium which can be used to store the desired information, and which canbe accessed by a computer.

The present disclosure provides a number of new features, including

-   -   1. Use of time pick, ray tracing inversion, and 3D STC to        determine ray path type of a selected arrival event.        -   a. Time pick of selected event may be performed manually or            via an automated procedure        -   b. For each ray path type, ray tracing inversion determines            position of reflector relative to well track and inclination            angle made between ray from reflector to receiver array and            the well track.            -   i. Error in ray tracing fit to time pick can be used to                help determine ray path type            -   ii. Ray tracing results can be used to determine                relative dip of the reflector.        -   c. 3D STC used to analyze coherence and consistency of            waveforms at measured depth positions along selected event            time pick for range of inclination angles determined in step            b.            -   i. Variance of 3D STC peak locations and sum of 3D STC                peak values can also be used to help determine ray path                type.            -   ii. Mean 3D STC peak location values can be used to                determine azimuth of the reflector.    -   2. Results of time pick, ray tracing inversion, and 3D STC        analysis can be used to        -   a. Create overlay of single sensor, common ring, and common            azimuth views of the waveforms with color-coded markings            indicating the position of the reflected arrival events.            Color-coding indicates the ray path type.        -   b. Create log panels showing color coded arrival event            attributes (reflector azimuth, true dip and azimuth (via            tadpoles), relative dip of reflector, receiver index) as a            function of measured depth. Tadpole logs, in particular, can            be compared with those produced via borehole image logs such            as FMI.            -   i. Group arrival events according to their ray path                type, measured depth position, and reflector azimuth and                to configure their corresponding migration parameters.        -   c. Create summaries of reflector true 3D dip and azimuth in            the form of stereonet plots, Rose diagrams which can be            compared to similar diagrams produced using FMI or other            borehole image logs.        -   d. Create display maps that convey the two-dimensional and            three-dimensional locations of the reflectors relative to            the well trajectory. The information conveyed by the display            maps can be overlaid on a migration result in order to            confirm the validity of specific migration image features            and to associate them with particular waveform features.        -   e. Output a display map conveying three-dimensional position            of the reflectors and/or information describing            three-dimensional position and orientation of the reflectors            (such as relative dip, azimuth, offset from borehole,            measured depth of reflector's intercept with wellbore) which            is included in a spreadsheet (or other structured data            object(s)) to be used for reservoir modeling, visualization            and/or simulation applications.        -   f. Group arrival events together according to their ray path            type, migration aperture, 3D STC azimuth and other            parameters. Automatically generate migration parameters for            each group of arrival events for review by the user.    -   3. User can interactively review results of automated time        picking and ray tracing/3D STC procedures for a number of        possible ray path types and interactively promote/demote certain        ray path type interpretation.

Although a few example embodiments have been described in detail above,those skilled in the art will readily appreciate that many modificationsare possible in the example embodiments without materially departingfrom this disclosure. Accordingly, such modifications are intended to beincluded within the scope of this disclosure. Moreover, embodiments maybe performed in the absence of any component not explicitly describedherein.

What is claimed is:
 1. A method of identifying and characterizingstructures of interest in a formation traversed by a wellbore,comprising: a) operating a sonic logging tool in the wellbore totransmit acoustic signals that probe near wellbore formation structuresand to receive the acoustic signals; b) generating waveform dataassociated with the received acoustic signals as a function of measureddepth in the wellbore; c) processing the waveform data of b) to identifyat least one arrival event, wherein the arrival event is associated witha time pick; d) using a ray tracing inversion of points in the time pickof c) for each one of a number of possible raypath types to determinetwo-dimensional positions of a reflector corresponding to the time pickand the number of possible raypath types; e) processing waveform datacorresponding to the time pick of c) for at least one possible raypathtype of d) to determine at least one three-dimensional slowness-timecoherence representation of the waveform data corresponding to the timepick and the at least one possible raypath type; f) evaluating the atleast one three-dimensional slowness-time coherence representation of e)to determine a raypath type for the time pick and corresponding arrivalevent; g) determining an azimuth angle of a reflector corresponding tothe time pick based on the three-dimensional slowness-time coherencerepresentation of e) corresponding to both the time pick and the raypathtype for the time pick as determined in f); and h) determining andoutputting a three-dimensional position for the reflector correspondingto the time pick, wherein the three-dimensional position of thereflector is based on the two-dimensional position of the reflectordetermined from the ray tracing inversion of d) for the raypath type ofthe time pick as determined in f) and azimuth angle of the reflector asdetermined in g).
 2. A method according to claim 1, wherein: theoperations of c) identify a set of arrival events, wherein each arrivalevent is associated with a corresponding time pick; and the operationsof d) through g) are repeated for the time picks corresponding to theset of arrival events to determine and output three-dimensionalpositions for reflectors that correspond to the set of arrival events.3. A method according to claim 1, wherein: the ray tracing inversion ofd) varies and solves for two-dimensional position and relative dip of areflector having a PP or SS reflection raypath type where thetwo-dimensional position and relative dip of the reflector is specifiedby i) a polar angle made from a position along the sonic logging tooland ii) a polar distance offset from said position; and/or the raytracing inversion of d) varies and solves for two-dimensional positionand relative dip of a reflector having a PS or SP mode conversionraypath type where the two-dimensional position and relative dip of thereflector is specified by i) a position along the sonic logging tool andii) a polar angle made from said position.
 4. A method according toclaim 1, wherein: evaluating the at least one three-dimensionalslowness-time coherence representation of f) involves generating scoresfor a plurality of possible ray path types of the time pick based on atleast three-dimensional slowness-time coherence representations of e)corresponding to the time pick and the plurality of possible ray pathtypes, and evaluating the scores for the plurality of possible ray pathtypes of the time pick to determine a raypath type for the given timepick and corresponding arrival event.
 5. A method according to claim 4,wherein: the three-dimensional slowness-time coherence representationscorresponding to the time pick and the plurality of possible raypathtypes of e) include peak coherence values for the plurality of possibleraypath types; and the scores for the plurality of possible ray pathtypes of the time pick is based on the peak coherence values for theplurality of possible raypath types.
 6. A method according to claim 4,wherein: the three-dimensional slowness-time coherence representationscorresponding to the time pick and the plurality of possible raypathtypes of e) include peak inclination angles and peak azimuth angles forthe plurality of possible raypath types; and the scores for theplurality of possible ray path types of the time pick is based on thepeak inclination angles and peak azimuth angles for the plurality ofpossible raypath types.
 7. A method according to claim 4, wherein: theray tracing inversion of d) for the time pick and a particular raypathtype solves for a predicted time pick; and the scores for the pluralityof possible ray path types of the time pick is based on error valuesbetween the time pick and the predicted time picks for the plurality ofpossible raypath types.
 8. A method according to claim 1, furthercomprising: evaluating the at least one three-dimensional slowness-timecoherence representation of e) over a range of inclination angles andazimuth angles to determine an inclination angle and azimuth angle ofthe corresponding reflector; using the inclination angle and azimuthangle of the reflector to characterize orientation of the reflector; andoutputting the orientation of the reflector.
 9. A method according toclaim 1, wherein: the waveform data of b) is filtered to reduce theinterference of the direct borehole modes and other signals.
 10. Amethod according to claim 1, wherein: in the event that the ray tracinginversion of d) produces a plurality of two-dimensional reflectorpositions and raypath type pairs, the evaluation of the at least onethree-dimensional slowness-time coherence representation of e) is usedto select one of the plurality of two-dimensional reflector positionsand raypath type pairs.
 11. A method according to claim 1, wherein: theray tracing inversion used in d) represents the reflector as a simplelinear reflector.
 12. A method according to claim 1, wherein: thearrival event is selected manually by user-input.
 13. A method accordingto claim 1, wherein: the arrival event is selected by an automaticprocedure.
 14. A method according to claim 13, wherein the automaticprocedure involves: applying a tau-P transform to the waveform data;selecting a set of peaks in resulting data in the Tau-P domain, whereineach peak in the set of peaks corresponds to a line in the waveform datafor a candidate arrival event; and for each peak or correspondingcandidate arrival event, defining a time corridor window correspondingto the candidate arrival event and associated line, and correlating thewaveform data within the time corridor window with a function toidentify a time pick for the candidate arrival event.
 15. A methodaccording to claim 14, wherein: the waveform data values within a timecorridor window are designated as w(i,j) with i being the time index andj being the measured depth index, T is a duration of the time corridorwindow, and M is the length of a short window in measured depth; and thefunction has the form

(j)=E_(c)(j)coh(j) where E_(c)(j) is coherent energy given byE _(c)(j)=Σ_(i=1) ^(T)(Σ_(Δ=−M/2) ^(M/2) w(i,j+Δ))², E_(i)(j) is incoherent energy given byE _(i)(j)=Σ_(i=1) ^(T)Σ_(Δ=−M/2) ^(M/2) w ²(i,j+Δ), coh(j) are coherencefunctions given bycoh(j)=√{square root over (E _(c)(j)/ME _(i)(j))}.
 16. A methodaccording to claim 1, wherein: the ray racing inversion of d) isperformed on waveform data corresponding to the time pick of a candidatearrival event and derived from received signals for a given acousticreceiver of the sonic logging tool.
 17. A method according to claim 1,wherein: the ray racing inversion of d) is performed on waveform datacorresponding to the time pick of a candidate arrival event and derivedfrom received signals for a plurality of acoustic receivers of the soniclogging tool that are spaced in azimuth about the circumference of thesonic logging tool.
 18. A method according to claim 1, furthercomprising: generating at least one display that conveys azimuthposition for the arrival event (for example, as part of an overlay ofmarkings on a single sensor, common ring, and common azimuth view ofwaveform data).
 19. A method according to claim 1, further comprising:generating at least one display that conveys ray-path type for thearrival event as determined in f) (for example, as part of an overlay ofmarkings on a single sensor, common ring, and common azimuth view ofwaveform data).
 20. A method according to claim 19, wherein: the displayincludes features (such as color) to convey ray-path type for thearrival event.
 21. A method according to claim 1, further comprising:generating at least one display that conveys attributes of the reflectoror arrival event (such as reflector azimuth, reflector dip orientation,receiver index of arrival event) as a function of measured depth.
 22. Amethod according to claim 22, wherein: the display includes features(such as color) to convey ray-path type for the reflector or arrivalevent.
 23. A method according to claim 1, further comprising: generatingleast one display that conveys two-dimensional or three-dimensionallocation of at least one reflector relative to well trajectory.
 24. Amethod according to claim 23, wherein: the display includes features(such as color) to convey ray-path type for the at least one reflector.25. A method according to claim 1, further comprising generating aplurality of displays with at least one index label associated with acorresponding reflector or arrival event in order to showncorrespondence of the reflector or arrival event across the plurality ofdisplays.
 26. A method according to claim 1, further comprising:outputting three-dimensional position and/or orientation of thereflector in a structured format selected from the group consisting of aspreadsheet, tadpole plot, stereonet view, Rose diagram, and combinationthereof.
 27. A method according to claim 26, further comprising: thethree-dimensional position and/or orientation of the reflector isspecified in a true earth coordinate system.
 28. A method according toclaim 26, further comprising: displaying the structured format inconjunction with information that characterizes three-dimensionalposition and/or orientation of the reflector derived from processing offormation resistivity measurements (such as an FMI image of the sameinterval of measured depth in the wellbore).
 29. A method according toclaim 1, further comprising: outputting three-dimensional positionand/or orientation of the reflector for reservoir modeling,visualization and/or simulation applications.
 30. A method according toclaim 1, further comprising: interacting with a user to select ordeselect at least one particular ray type that is part of the raytracing inversions of d) and the processing of e).
 31. A methodaccording to claim 1, wherein: the reflectors represent formationstructures of interest (such as fractures, bed boundaries and faults).